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4
.gitignore
vendored
4
.gitignore
vendored
@@ -1,3 +1,5 @@
|
||||
.idea/
|
||||
__pycache__/
|
||||
__pycache__/
|
||||
Save/
|
||||
**/__pycache__/
|
||||
|
||||
|
||||
46
Empty.json
Normal file
46
Empty.json
Normal file
@@ -0,0 +1,46 @@
|
||||
{
|
||||
"strategy_name": "Empty",
|
||||
"params": {
|
||||
"roi": {
|
||||
"0": 5
|
||||
},
|
||||
"stoploss": {
|
||||
"stoploss": -1.0
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": false,
|
||||
"trailing_stop_positive": null,
|
||||
"trailing_stop_positive_offset": 0.0,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 20
|
||||
},
|
||||
"protection": {
|
||||
"b30_indicateur": "sma36",
|
||||
"baisse": 0.33,
|
||||
"drop_from_last_entry": -0.03
|
||||
},
|
||||
"buy": {
|
||||
"buy_deriv1_sma12d": -0.04,
|
||||
"buy_deriv1_sma5d": -0.02,
|
||||
"buy_deriv1_sma60": 0.001,
|
||||
"buy_deriv2_sma12d": -0.02,
|
||||
"buy_deriv2_sma5d": 0.0,
|
||||
"buy_deriv2_sma60": -0.001,
|
||||
"buy_longue": 160,
|
||||
"buy_longue_derive": "sma80_deriv1_1d",
|
||||
"start_bear_deriv1": -0.004,
|
||||
"start_bear_deriv2": 0.002,
|
||||
"start_bear_indicator": "sma80",
|
||||
"start_bull_deriv1": 0.001,
|
||||
"start_bull_deriv2": -0.002,
|
||||
"start_bull_indicator": "sma120"
|
||||
},
|
||||
"sell": {
|
||||
"sell_score_indicator": "sma24_score"
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2026-02-28 17:28:20.867470+00:00"
|
||||
}
|
||||
512
Empty.txt
Normal file
512
Empty.txt
Normal file
@@ -0,0 +1,512 @@
|
||||
👉 Cette version est objectivement supérieure.
|
||||
|
||||
Même si le CAGR bouge légèrement, tout le reste s’améliore fortement.
|
||||
|
||||
📊 Comparaison claire
|
||||
Metric Avant Maintenant
|
||||
Trades 95 53
|
||||
CAGR 21.38% 22.45%
|
||||
Profit Factor 1.89 2.85
|
||||
SQN 1.87 2.38
|
||||
Winrate 48% 62%
|
||||
Max DD 21.97% 7.37%
|
||||
DD duration 309 jours 28 jours
|
||||
Calmar 7.08 22.51
|
||||
🔥 Ce que ça veut dire
|
||||
|
||||
Le filtre structurel (SMA200 1D) a fait exactement ce qu’on attendait :
|
||||
|
||||
Moins de trades
|
||||
Beaucoup moins de trades pourris
|
||||
Equity beaucoup plus lisse
|
||||
Bear market 2022 quasiment neutralisé
|
||||
Et surtout :
|
||||
👉 Le drawdown long a disparu.
|
||||
C’est LE point critique.
|
||||
🧠 Lecture professionnelle
|
||||
Cette version est :
|
||||
Plus robuste
|
||||
Plus stable
|
||||
Plus exploitable en réel
|
||||
Psychologiquement viable
|
||||
Un DD de 309 jours casse un trader.
|
||||
Un DD de 28 jours est gérable.
|
||||
|
||||
🎯 Ce qui est très intéressant
|
||||
|
||||
Regarde ça :
|
||||
Worst trade passe de -4.55% → -4.10%
|
||||
Profit factor explose à 2.85
|
||||
Expectancy 24.77 (énorme)
|
||||
Ça veut dire que :
|
||||
👉 Tu trades maintenant uniquement les phases vraiment favorables.
|
||||
📉 Le “un peu moins de gain”
|
||||
En réalité ton CAGR est même légèrement plus haut (22.45 vs 21.38).
|
||||
Mais même si c’était un peu plus bas :
|
||||
Réduire le drawdown de 22% à 7% vaut largement 2–3% de CAGR.
|
||||
Toujours.
|
||||
|
||||
🧠 Ce que tu viens de faire Tu as transformé une stratégie Opportuniste en Régime-aware Et ça change tout.
|
||||
|
||||
⚠️ Maintenant la vraie question
|
||||
|
||||
Veux-tu :
|
||||
|
||||
🔬 Tester robustness (walk-forward)
|
||||
📈 Ajouter un léger boost de performance
|
||||
⚖️ Ajouter un sizing adaptatif selon régime
|
||||
🚀 Ajouter un second actif
|
||||
Parce que là, on est à l’étape “optimisation fine”, plus “réparation”.
|
||||
=============================================================================
|
||||
Bon.
|
||||
|
||||
Là on va parler sérieusement 😄
|
||||
Parce que tes résultats ne sont pas mauvais du tout.
|
||||
|
||||
📊 Lecture objective de ton backtest
|
||||
✅ Points très solides
|
||||
CAGR 35% sur 4 ans → très correct
|
||||
Max DD 10.6% → excellent
|
||||
Profit factor 1.86 → bon edge
|
||||
Calmar 29 → extrêmement bon (drawdown très maîtrisé)
|
||||
Max underwater 11% → propre
|
||||
Pas d’explosion du risk
|
||||
|
||||
Franchement, c’est une stratégie saine.
|
||||
|
||||
⚠️ Le vrai problème
|
||||
|
||||
Regarde ces deux chiffres :
|
||||
Sharpe : 0.21
|
||||
Sortino : 1.31
|
||||
Sharpe très bas → equity curve irrégulière
|
||||
Sortino correct → pertes contrôlées
|
||||
|
||||
Ce que ça dit :
|
||||
|
||||
Tu gagnes bien… mais de manière irrégulière.
|
||||
|
||||
🎯 Ce qui saute aux yeux
|
||||
92 trades en 4 ans
|
||||
Ça fait :
|
||||
23 trades par an
|
||||
C’est extrêmement peu.
|
||||
Donc :
|
||||
Quelques gros trades font la perf
|
||||
Beaucoup de périodes mortes
|
||||
Equity plate pendant longtemps
|
||||
Ce n’est pas un problème technique. C’est un problème structurel.
|
||||
🧠Ce que ton système est en réalité Ce n’est pas un trend following classique.
|
||||
|
||||
C’est Un breakout / impulsion filtrée daily
|
||||
|
||||
Regarde :
|
||||
Best trade : +48%
|
||||
Avg winner duration : 6 jours
|
||||
Certains winners : 85 jours
|
||||
Tu attrapes des swings longs.
|
||||
Et tu survis entre deux cycles.
|
||||
|
||||
C’est cohérent.
|
||||
🚨 Pourquoi tu as l’impression que rien n’améliore ?
|
||||
Parce que tu es déjà dans un bon équilibre.
|
||||
Quand une stratégie est :
|
||||
Stable
|
||||
Faible DD
|
||||
PF proche de 2
|
||||
|
||||
👉 Les gains d’optimisation deviennent marginaux.
|
||||
|
||||
Tu n’es plus dans la phase “je corrige une erreur”.
|
||||
Tu es dans la phase “je modifie l’ADN”.
|
||||
|
||||
🔍 Ce que je vois comme axe réel d’amélioration
|
||||
|
||||
Pas le stoploss.
|
||||
Pas le RSI adaptatif.
|
||||
|
||||
Le vrai levier est ici :
|
||||
Winrate = 48.9%
|
||||
Tu es pile à l’équilibre.
|
||||
Si tu passes à 55% avec même RR,
|
||||
ta courbe devient radicalement plus stable.
|
||||
🎯 La vraie question
|
||||
|
||||
Tes losers durent :
|
||||
1d 12h en moyenne.
|
||||
Tes winners :
|
||||
6d 12h.
|
||||
Donc ton edge vient du R:R, pas du winrate.
|
||||
|
||||
💡 Deux directions possibles
|
||||
OPTION 1 — Rendre le système plus agressif
|
||||
Plus d’entrées
|
||||
Plus de trades
|
||||
Légèrement plus de DD
|
||||
|
||||
Sharpe augmente
|
||||
|
||||
→ Transformer ton système en semi-trend.
|
||||
|
||||
OPTION 2 — Assumer que c’est un cycle catcher
|
||||
|
||||
Et optimiser :
|
||||
La sortie en fin de cycle
|
||||
Le trailing dynamique
|
||||
La protection en marché neutre
|
||||
🎯 Analyse importante
|
||||
Market change : +44%
|
||||
Toi : +247%
|
||||
|
||||
Tu as largement surperformé le marché.
|
||||
|
||||
Donc ton edge est réel.
|
||||
|
||||
💬 Question importante
|
||||
|
||||
Tu veux :
|
||||
|
||||
A) Maximiser le CAGR
|
||||
B) Maximiser la stabilité (Sharpe > 1)
|
||||
C) Réduire encore le drawdown
|
||||
D) Multiplier les trades
|
||||
|
||||
Parce que selon ton objectif, on ne touche pas aux mêmes leviers.
|
||||
|
||||
Et là tu es à un point où chaque modification change la philosophie du système.
|
||||
|
||||
SUMMARY METRICS
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Metric ┃ Value ┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ Backtesting from │ 2022-01-01 00:00:00 │
|
||||
│ Backtesting to │ 2026-02-20 00:00:00 │
|
||||
│ Trading Mode │ Spot │
|
||||
│ Max open trades │ 1 │
|
||||
│ │ │
|
||||
│ Total/Daily Avg Trades │ 92 / 0.06 │
|
||||
│ Starting balance │ 1000 USDT │
|
||||
│ Final balance │ 3474.507 USDT │
|
||||
│ Absolute profit │ 2474.507 USDT │
|
||||
│ Total profit % │ 247.45% │
|
||||
│ CAGR % │ 35.10% │
|
||||
│ Sortino │ 1.31 │
|
||||
│ Sharpe │ 0.21 │
|
||||
│ Calmar │ 29.34 │
|
||||
│ SQN │ 1.76 │
|
||||
│ Profit factor │ 1.86 │
|
||||
│ Expectancy (Ratio) │ 26.90 (0.44) │
|
||||
│ Avg. daily profit │ 1.638 USDT │
|
||||
│ Avg. stake amount │ 2413.03 USDT │
|
||||
│ Total trade volume │ 447365.827 USDT │
|
||||
│ │ │
|
||||
│ Best Pair │ BTC/USDT 247.45% │
|
||||
│ Worst Pair │ BTC/USDT 247.45% │
|
||||
│ Best trade │ BTC/USDT 48.98% │
|
||||
│ Worst trade │ BTC/USDT -5.61% │
|
||||
│ Best day │ 806.579 USDT │
|
||||
│ Worst day │ -137.268 USDT │
|
||||
│ Days win/draw/lose │ 44 / 1328 / 47 │
|
||||
│ Min/Max/Avg. Duration Winners │ 1d 03:00 / 85d 12:00 / 6d 12:21 │
|
||||
│ Min/Max/Avg. Duration Losers │ 0d 02:00 / 5d 12:00 / 1d 12:28 │
|
||||
│ Max Consecutive Wins / Loss │ 4 / 4 │
|
||||
│ Rejected Entry signals │ 0 │
|
||||
│ Entry/Exit Timeouts │ 0 / 0 │
|
||||
│ │ │
|
||||
│ Min balance │ 981.947 USDT │
|
||||
│ Max balance │ 3630.59 USDT │
|
||||
│ Max % of account underwater │ 11.64% │
|
||||
│ Absolute drawdown │ 359.078 USDT (10.66%) │
|
||||
│ Drawdown duration │ 86 days 03:00:00 │
|
||||
│ Profit at drawdown start │ 2367.463 USDT │
|
||||
│ Profit at drawdown end │ 2008.385 USDT │
|
||||
│ Drawdown start │ 2025-01-19 22:00:00 │
|
||||
│ Drawdown end │ 2025-04-16 01:00:00 │
|
||||
│ Market change │ 44.09% │
|
||||
└───────────────────────────────┴─────────────────────────────────┘
|
||||
|
||||
Backtested 2022-01-01 00:00:00 -> 2026-02-20 00:00:00 | Max open trades : 1
|
||||
STRATEGY SUMMARY
|
||||
┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃
|
||||
┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ Empty │ 92 │ 1.62 │ 2474.507 │ 247.45 │ 3 days, 23:07:00 │ 45 0 47 48.9 │ 359.078 USDT 10.66% │
|
||||
└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┴──────────────────────┘
|
||||
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2023-01-10 17:00 |🟩Buy 0 | BTC | bull | 17302.55| 1000 | 0/0| - | 17302.15 | - | 17302 | 17302 | 1/1| 1000.0
|
||||
| 2023-02-09 22:00 |🟥Sell 43500 | BTC | B30 | 21856.3| - |260.9/395.52| 0.263 | 17302.15 | 0.263 | 17302 | 17302 | /1| -
|
||||
| 2023-02-15 00:00 |🟩Buy 7320 | BTC | bull | 22199.84| 1261 | 0/0| - | 22199.84 | - | 22199 | 17302 | 1/1| 1260.9
|
||||
| 2023-02-16 23:00 |🟥Sell 2820 | BTC | B30 | 23994.76| - |99.31/160.46| 0.081 | 22199.84 | 0.081 | 22199 | 17302 | /1| -
|
||||
| 2023-02-17 17:00 |🟩Buy 1080 | BTC | bull | 24091.21| 1360 | 0/0| - | 24091.21 | - | 24091 | 17302 | 1/1|1360.21
|
||||
| 2023-02-19 18:00 |🟥Sell 2940 | BTC | B30 | 24361.74| - |12.69/44.15| 0.011 | 24091.21 | 0.011 | 24091 | 17302 | /1| -
|
||||
| 2023-02-28 17:00 |🟩Buy 12900 | BTC | bull | 23456.05| 1373 | 0/0| - | 23456.05 | - | 24091 | 17302 | 1/1| 1372.9
|
||||
| 2023-03-03 02:00 |🟥Sell 3420 | BTC | sma60 | 22150.31| - |-79.09/21.4| -0.056 | 23456.05 | -0.056 | 24091 | 17302 | /1| -
|
||||
| 2023-03-16 09:00 |🟩Buy 19140 | BTC | bull | 24581.1| 1294 | 0/0| - | 24581.1 | - | 24581 | 17302 | 1/1| 1293.8
|
||||
| 2023-03-22 20:00 |🟥Sell 9300 | BTC | B30 | 26667.33| - |107.18/211.2| 0.085 | 24581.1 | 0.085 | 24581 | 17302 | /1| -
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2023-03-30 03:00 |🟩Buy 10500 | BTC | bull | 28964.76| 1401 | 0/0| - | 28964.76 | - | 28964 | 17302 | 1/1|1400.98
|
||||
| 2023-03-30 18:00 |🟥Sell 900 | BTC | sma60 | 27839.41| - | -57.17/0| -0.039 | 28964.76 | -0.039 | 28964 | 17302 | /1| -
|
||||
| 2023-03-31 15:00 |🟩Buy 1260 | BTC | bull | 28521.78| 1344 | 0/0| - | 28521.78 | - | 28964 | 17302 | 1/1|1343.81
|
||||
| 2023-04-02 20:00 |🟥Sell 3180 | BTC | sma60 | 27943.85| - |-29.89/1.49| -0.02 | 28521.78 | -0.02 | 28964 | 17302 | /1| -
|
||||
| 2023-04-13 04:00 |🟩Buy 14880 | BTC | bull | 30072.17| 1314 | 0/0| - | 30072.17 | - | 30072 | 17302 | 1/1|1313.92
|
||||
| 2023-04-15 02:00 |🟥Sell 2760 | BTC | B30 | 30370.01| - |10.37/35.37| 0.01 | 30072.17 | 0.01 | 30072 | 17302 | /1| -
|
||||
| 2023-04-26 00:00 |🟩Buy 15720 | BTC | bull | 28300.8| 1324 | 0/0| - | 28300.79 | - | 30072 | 17302 | 1/1| 1324.3
|
||||
| 2023-04-28 14:00 |🟥Sell 3720 | BTC | B30 | 29029.48| - |31.41/75.16| 0.026 | 28300.79 | 0.026 | 30072 | 17302 | /1| -
|
||||
| 2023-04-28 23:00 |🟩Buy 540 | BTC | bull | 29345.35| 1356 | 0/0| - | 29345.34 | - | 30072 | 17302 | 1/1|1355.71
|
||||
| 2023-05-01 02:00 |🟥Sell 3060 | BTC | sma60 | 28504.36| - |-41.52/14.46| -0.029 | 29345.34 | -0.029 | 30072 | 17302 | /1| -
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2023-05-30 12:00 |🟩Buy 42360 | BTC | bull | 28021.39| 1314 | 0/0| - | 28021.4 | - | 30072 | 17302 | 1/1|1314.19
|
||||
| 2023-05-31 05:00 |🟥Sell 1020 | BTC | sma60 | 27257.7| - | -38.4/0| -0.027 | 28021.4 | -0.027 | 30072 | 17302 | /1| -
|
||||
| 2023-06-19 19:00 |🟩Buy 28200 | BTC | bull | 26495.99| 1276 | 0/0| - | 26496.0 | - | 30072 | 17302 | 1/1|1275.79
|
||||
| 2023-07-17 19:00 |🟥Sell 40320 | BTC | B30 | 29802.39| - |156.49/243.25| 0.125 | 26496.0 | 0.125 | 30072 | 17302 | /1| -
|
||||
| 2023-07-19 05:00 |🟩Buy 2040 | BTC | bull | 30099.08| 1432 | 0/0| - | 30099.08 | - | 30099 | 17302 | 1/1|1432.28
|
||||
| 2023-07-24 10:00 |🟥Sell 7500 | BTC | sma60 | 29178.01| - |-46.65/8.02| -0.031 | 30099.08 | -0.031 | 30099 | 17302 | /1| -
|
||||
| 2023-09-20 03:00 |🟩Buy 83100 | BTC | bull | 27165.48| 1386 | 0/0| - | 27165.48 | - | 30099 | 17302 | 1/1|1385.64
|
||||
| 2023-09-21 11:00 |🟥Sell 1920 | BTC | sma60 | 26649.89| - | -29.04/0| -0.019 | 27165.48 | -0.019 | 30099 | 17302 | /1| -
|
||||
| 2023-10-01 08:00 |🟩Buy 14220 | BTC | bull | 27107.94| 1357 | 0/0| - | 27107.95 | - | 30099 | 17302 | 1/1| 1356.6
|
||||
| 2023-10-02 22:00 |🟥Sell 2280 | BTC | B30 | 27472.22| - |15.5/69.08| 0.013 | 27107.95 | 0.013 | 30099 | 17302 | /1| -
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2023-10-15 20:00 |🟩Buy 18600 | BTC | bull | 27043.75| 1372 | 0/0| - | 27043.75 | - | 30099 | 17302 | 1/1| 1372.1
|
||||
| 2023-10-18 15:00 |🟥Sell 4020 | BTC | B30 | 28190.49| - |55.37/82.75| 0.042 | 27043.75 | 0.042 | 30099 | 17302 | /1| -
|
||||
| 2023-10-19 12:00 |🟩Buy 1260 | BTC | bull | 28450.7| 1427 | 0/0| - | 28450.69 | - | 30099 | 17302 | 1/1|1427.47
|
||||
| 2024-01-15 00:00 |🟥Sell 126000 | BTC | B30 | 41732.35| - |662.82/1005.58| 0.467 | 28450.69 | 0.467 | 30099 | 17302 | /1| -
|
||||
| 2024-01-27 22:00 |🟩Buy 18600 | BTC | bull | 42150.59| 2090 | 0/0| - | 42150.59 | - | 42150 | 17302 | 1/1|2090.29
|
||||
| 2024-01-31 00:00 |🟥Sell 4440 | BTC | B30 | 42941.1| - |34.98/68.37| 0.019 | 42150.59 | 0.019 | 42150 | 17302 | /1| -
|
||||
| 2024-02-08 00:00 |🟩Buy 11520 | BTC | bull | 44349.6| 2125 | 0/0| - | 44349.6 | - | 44349 | 17302 | 1/1|2125.27
|
||||
| 2024-03-19 09:00 |🟥Sell 58140 | BTC | B30 | 63464.0| - |910.78/1394.94| 0.431 | 44349.6 | 0.431 | 44349 | 17302 | /1| -
|
||||
| 2024-03-24 11:00 |🟩Buy 7320 | BTC | bull | 65098.01| 3036 | 0/0| - | 65098.01 | - | 65098 | 17302 | 1/1|3036.05
|
||||
| 2024-03-27 16:00 |🟥Sell 4620 | BTC | B30 | 68874.0| 1 |169.83/275.49| 0.058 | 65098.01 | 0.058 | 65098 | 17302 | /1| -
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2024-03-28 07:00 |🟩Buy 900 | BTC | bull | 70363.74| 3206 | 0/0| - | 70363.74 | - | 70363 | 17302 | 1/1|3205.87
|
||||
| 2024-04-01 15:00 |🟥Sell 6240 | BTC | sma60 | 68906.07| - |-72.76/47.03| -0.021 | 70363.74 | -0.021 | 70363 | 17302 | /1| -
|
||||
| 2024-04-08 08:00 |🟩Buy 9660 | BTC | bull | 70698.64| 3133 | 0/0| - | 70698.63 | - | 70698 | 17302 | 1/1|3133.12
|
||||
| 2024-04-09 15:00 |🟥Sell 1860 | BTC | sma60 | 69358.32| - |-65.6/69.22| -0.019 | 70698.63 | -0.019 | 70698 | 17302 | /1| -
|
||||
| 2024-04-10 20:00 |🟩Buy 1740 | BTC | bull | 70084.0| 3068 | 0/0| - | 70083.99 | - | 70698 | 17302 | 1/1|3067.52
|
||||
| 2024-04-12 18:00 |🟥Sell 2760 | BTC | sma60 | 68140.02| 1 |-91.12/38.98| -0.028 | 70083.99 | -0.028 | 70698 | 17302 | /1| -
|
||||
| 2024-04-24 05:00 |🟩Buy 16500 | BTC | bull | 67008.0| 2976 | 0/0| - | 67008.0 | - | 70698 | 17302 | 1/1| 2976.4
|
||||
| 2024-04-24 15:00 |🟥Sell 600 | BTC | sma60 | 65234.85| 1 | -84.62/0| -0.026 | 67008.0 | -0.026 | 70698 | 17302 | /1| -
|
||||
| 2024-05-06 08:00 |🟩Buy 16860 | BTC | bull | 64540.75| 2892 | 0/0| - | 64540.75 | - | 70698 | 17302 | 1/1|2891.79
|
||||
| 2024-05-06 15:00 |🟥Sell 420 | BTC | sma60 | 63271.97| - |-62.57/22.21| -0.02 | 64540.75 | -0.02 | 70698 | 17302 | /1| -
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2024-05-07 10:00 |🟩Buy 1140 | BTC | bull | 64119.98| 2829 | 0/0| - | 64119.98 | - | 70698 | 17302 | 1/1|2829.22
|
||||
| 2024-05-07 23:00 |🟥Sell 780 | BTC | sma60 | 62850.92| - | -61.59/0| -0.02 | 64119.98 | -0.02 | 70698 | 17302 | /1| -
|
||||
| 2024-05-17 08:00 |🟩Buy 13500 | BTC | bull | 66386.93| 2768 | 0/0| - | 66386.94 | - | 70698 | 17302 | 1/1|2767.63
|
||||
| 2024-05-19 15:00 |🟥Sell 3300 | BTC | B30 | 66928.0| 1 |17.0/33.73| 0.008 | 66386.94 | 0.008 | 70698 | 17302 | /1| -
|
||||
| 2024-05-27 16:00 |🟩Buy 11580 | BTC | bull | 70373.86| 2785 | 0/0| - | 70373.85 | - | 70698 | 17302 | 1/1|2784.62
|
||||
| 2024-05-28 02:00 |🟥Sell 600 | BTC | sma60 | 68614.11| 1 | -75.11/0| -0.025 | 70373.85 | -0.025 | 70698 | 17302 | /1| -
|
||||
| 2024-06-05 01:00 |🟩Buy 11460 | BTC | bull | 70775.99| 2710 | 0/0| - | 70776.0 | - | 70776 | 17302 | 1/1|2709.51
|
||||
| 2024-06-07 19:00 |🟥Sell 3960 | BTC | sma60 | 69154.43| - |-67.43/29.92| -0.023 | 70776.0 | -0.023 | 70776 | 17302 | /1| -
|
||||
| 2024-07-15 00:00 |🟩Buy 53580 | BTC | bull | 60797.91| 2642 | 0/0| - | 60797.91 | - | 70776 | 17302 | 1/1|2642.08
|
||||
| 2024-07-16 09:00 |🟥Sell 1980 | BTC | B30 | 62820.67| - |82.52/170.52| 0.033 | 60797.91 | 0.033 | 70776 | 17302 | /1| -
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2024-07-16 17:00 |🟩Buy 480 | BTC | bull | 64500.0| 2725 | 0/0| - | 64500.0 | - | 70776 | 17302 | 1/1| 2724.6
|
||||
| 2024-07-22 15:00 |🟥Sell 8520 | BTC | B30 | 66712.0| - |87.89/150.43| 0.034 | 64500.0 | 0.034 | 70776 | 17302 | /1| -
|
||||
| 2024-07-27 00:00 |🟩Buy 6300 | BTC | bull | 67908.0| 2812 | 0/0| - | 67907.99 | - | 70776 | 17302 | 1/1|2812.49
|
||||
| 2024-07-30 01:00 |🟥Sell 4380 | BTC | sma60 | 66610.0| - |-59.32/72.62| -0.019 | 67907.99 | -0.019 | 70776 | 17302 | /1| -
|
||||
| 2024-08-25 23:00 |🟩Buy 38760 | BTC | bull | 64482.0| 2753 | 0/0| - | 64482.01 | - | 70776 | 17302 | 1/1|2753.17
|
||||
| 2024-08-26 22:00 |🟥Sell 1380 | BTC | sma60 | 63155.81| - | -62.06/0| -0.021 | 64482.01 | -0.021 | 70776 | 17302 | /1| -
|
||||
| 2024-09-14 00:00 |🟩Buy 26040 | BTC | bull | 60497.99| 2691 | 0/0| - | 60498.0 | - | 70776 | 17302 | 1/1| 2691.1
|
||||
| 2024-09-16 00:00 |🟥Sell 2880 | BTC | sma60 | 59132.0| - | -66.08/0| -0.023 | 60498.0 | -0.023 | 70776 | 17302 | /1| -
|
||||
| 2024-09-19 00:00 |🟩Buy 4320 | BTC | bull | 61759.98| 2625 | 0/0| - | 61759.99 | - | 70776 | 17302 | 1/1|2625.02
|
||||
| 2024-09-20 18:00 |🟥Sell 2520 | BTC | B30 | 62704.0| - |34.83/83.22| 0.015 | 61759.99 | 0.015 | 70776 | 17302 | /1| -
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2024-09-28 04:00 |🟩Buy 10680 | BTC | bull | 66063.76| 2660 | 0/0| - | 66063.76 | - | 70776 | 17302 | 1/1|2659.86
|
||||
| 2024-09-30 02:00 |🟥Sell 2760 | BTC | sma60 | 64769.99| - | -57.35/0| -0.02 | 66063.76 | -0.02 | 70776 | 17302 | /1| -
|
||||
| 2024-10-14 05:00 |🟩Buy 20340 | BTC | bull | 64137.99| 2603 | 0/0| - | 64137.99 | - | 70776 | 17302 | 1/1| 2602.5
|
||||
| 2024-10-21 14:00 |🟥Sell 10620 | BTC | B30 | 67312.0| - |123.44/200.4| 0.049 | 64137.99 | 0.049 | 70776 | 17302 | /1| -
|
||||
| 2024-10-27 15:00 |🟩Buy 8700 | BTC | bull | 67696.0| 2726 | 0/0| - | 67695.99 | - | 70776 | 17302 | 1/1|2725.94
|
||||
| 2024-10-31 14:00 |🟥Sell 5700 | BTC | B30 | 71308.0| - |139.82/214.71| 0.053 | 67695.99 | 0.053 | 70776 | 17302 | /1| -
|
||||
| 2024-11-06 01:00 |🟩Buy 7860 | BTC | bull | 71096.03| 2866 | 0/0| - | 71096.04 | - | 71096 | 17302 | 1/1|2865.76
|
||||
| 2024-12-20 09:00 |🟥Sell 63840 | BTC | B30 | 95473.74| 1 |975.71/1490.41| 0.343 | 71096.04 | 0.343 | 71096 | 17302 | /1| -
|
||||
| 2024-12-26 00:00 |🟩Buy 8100 | BTC | bull | 99429.61| 3841 | 0/0| - | 99429.6 | - | 99429 | 17302 | 1/1|3841.47
|
||||
| 2024-12-26 09:00 |🟥Sell 540 | BTC | sma60 | 95878.56| 1 | -144.72/0| -0.036 | 99429.6 | -0.036 | 99429 | 17302 | /1| -
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2025-01-05 23:00 |🟩Buy 15240 | BTC | bull | 98695.21| 3697 | 0/0| - | 98695.21 | - | 99429 | 17302 | 1/1|3696.75
|
||||
| 2025-01-07 13:00 |🟥Sell 2280 | BTC | B30 |100750.89| 1 |69.52/125.06| 0.021 | 98695.21 | 0.021 | 99429 | 17302 | /1| -
|
||||
| 2025-01-13 22:00 |🟩Buy 9180 | BTC | bull | 94193.49| 3766 | 0/0| - | 94193.49 | - | 99429 | 17302 | 1/1|3766.26
|
||||
| 2025-01-16 15:00 |🟥Sell 3900 | BTC | B30 | 97618.89| - |129.28/244.24| 0.036 | 94193.49 | 0.036 | 99429 | 17302 | /1| -
|
||||
| 2025-01-17 02:00 |🟩Buy 660 | BTC | bull | 101659.8| 3896 | 0/0| - | 101659.8 | - | 101659 | 17302 | 1/1|3895.54
|
||||
| 2025-01-19 22:00 |🟥Sell 4080 | BTC | B30 |103684.44| 1 |69.7/168.86| 0.02 | 101659.8 | 0.02 | 101659 | 17302 | /1| -
|
||||
| 2025-01-23 18:00 |🟩Buy 5520 | BTC | bull |105927.78| 3965 | 0/0| - | 105927.78 | - | 105927 | 17302 | 1/1|3965.24
|
||||
| 2025-01-23 20:00 |🟥Sell 120 | BTC | sma60 |103770.04| - | -88.61/0| -0.02 | 105927.78 | -0.02 | 105927 | 17302 | /1| -
|
||||
| 2025-01-31 17:00 |🟩Buy 11340 | BTC | bull |105081.86| 3877 | 0/0| - | 105081.87 | - | 105927 | 17302 | 1/1|3876.63
|
||||
| 2025-01-31 19:00 |🟥Sell 120 | BTC | sma60 |102627.29| - | -98.21/0| -0.023 | 105081.87 | -0.023 | 105927 | 17302 | /1| -
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2025-04-14 04:00 |🟩Buy 104220 | BTC | bull | 84566.04| 3778 | 0/0| - | 84566.03 | - | 105927 | 17302 | 1/1|3778.41
|
||||
| 2025-06-05 20:00 |🟥Sell 75840 | BTC | B30 |101914.77| - |766.81/1207.18| 0.205 | 84566.03 | 0.205 | 105927 | 17302 | /1| -
|
||||
| 2025-06-08 17:00 |🟩Buy 4140 | BTC | bull | 106190.0| 4545 | 0/0| - | 106189.99 | - | 106189 | 17302 | 1/1|4545.22
|
||||
| 2025-06-11 18:00 |🟥Sell 4380 | BTC | B30 |108984.72| - |110.4/165.55| 0.026 | 106189.99 | 0.026 | 106189 | 17302 | /1| -
|
||||
| 2025-06-24 00:00 |🟩Buy 17640 | BTC | bull |105333.94| 4656 | 0/0| - | 105333.93 | - | 106189 | 17302 | 1/1|4655.63
|
||||
| 2025-06-26 14:00 |🟥Sell 3720 | BTC | B30 |106960.01| 1 |62.47/105.0| 0.015 | 105333.93 | 0.015 | 106189 | 17302 | /1| -
|
||||
| 2025-07-09 14:00 |🟩Buy 18720 | BTC | bull |109183.99| 4718 | 0/0| - | 109184.0 | - | 109184 | 17302 | 1/1| 4718.1
|
||||
| 2025-07-15 03:00 |🟥Sell 7980 | BTC | B30 |118251.05| - |381.96/575.56| 0.083 | 109184.0 | 0.083 | 109184 | 17302 | /1| -
|
||||
| 2025-07-23 00:00 |🟩Buy 11340 | BTC | bull |119954.43| 5100 | 0/0| - | 119954.42 | - | 119954 | 17302 | 1/1|5100.06
|
||||
| 2025-07-23 14:00 |🟥Sell 840 | BTC | sma60 |117381.44| 1 | -119.47/0| -0.021 | 119954.42 | -0.021 | 119954 | 17302 | /1| -
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2025-07-24 02:00 |🟩Buy 720 | BTC | bull | 119094.4| 4981 | 0/0| - | 119094.4 | - | 119954 | 17302 | 1/1| 4980.6
|
||||
| 2025-07-25 03:00 |🟥Sell 1500 | BTC | sma60 |116353.67| - | -124.46/0| -0.023 | 119094.4 | -0.023 | 119954 | 17302 | /1| -
|
||||
| 2025-08-09 11:00 |🟩Buy 22080 | BTC | bull |117424.16| 4856 | 0/0| - | 117424.16 | - | 119954 | 17302 | 1/1|4856.13
|
||||
| 2025-08-11 18:00 |🟥Sell 3300 | BTC | B30 | 119500.0| 1 |76.04/191.73| 0.018 | 117424.16 | 0.018 | 119954 | 17302 | /1| -
|
||||
| 2025-08-12 17:00 |🟩Buy 1380 | BTC | bull |119647.69| 4932 | 0/0| - | 119647.7 | - | 119954 | 17302 | 1/1|4932.17
|
||||
| 2025-08-14 11:00 |🟥Sell 2520 | BTC | B30 | 120933.0| - |43.06/163.09| 0.011 | 119647.7 | 0.011 | 119954 | 17302 | /1| -
|
||||
| 2025-09-02 03:00 |🟩Buy 26880 | BTC | bull | 110246.0| 4975 | 0/0| - | 110246.01 | - | 119954 | 17302 | 1/1|4975.23
|
||||
| 2025-09-04 04:00 |🟥Sell 2940 | BTC | B30 |111148.97| 1 |30.75/83.2| 0.008 | 110246.01 | 0.008 | 119954 | 17302 | /1| -
|
||||
| 2025-09-05 00:00 |🟩Buy 1200 | BTC | bull |110730.87| 5006 | 0/0| - | 110730.87 | - | 119954 | 17302 | 1/1|5005.99
|
||||
| 2025-09-09 01:00 |🟥Sell 5820 | BTC | B30 |111650.03| 1 |31.49/102.15| 0.008 | 110730.87 | 0.008 | 119954 | 17302 | /1| -
|
||||
| Date | Action |Pair | Trade Type | Rate | Dispo | Profit | Pct | max_touch | last_lost | last_max| last_max| Buys| Stake | rsi|
|
||||
+------------------+------------+-----+--------------------+---------+--------+------------+--------+-------------+--------------+------------------+-----+-------++------+-------+-----+-----+-----+-----+-----+-----+
|
||||
| 2025-09-09 09:00 |🟩Buy 480 | BTC | bull |112992.69| 5037 | 0/0| - | 112992.69 | - | 119954 | 17302 | 1/1|5037.48
|
||||
| 2025-09-09 16:00 |🟥Sell 420 | BTC | sma60 |110880.76| - | -104.13/0| -0.019 | 112992.69 | -0.019 | 119954 | 17302 | /1| -
|
||||
| 2025-09-10 09:00 |🟩Buy 1020 | BTC | bull |112370.07| 4933 | 0/0| - | 112370.08 | - | 119954 | 17302 | 1/1|4933.35
|
||||
| 2025-09-14 16:00 |🟥Sell 6180 | BTC | B30 |115224.01| - |115.3/177.07| 0.025 | 112370.08 | 0.025 | 119954 | 17302 | /1| -
|
||||
| 2025-09-18 01:00 |🟩Buy 4860 | BTC | bull |116350.65| 5049 | 0/0| - | 116350.65 | - | 119954 | 17302 | 1/1|5048.65
|
||||
| 2025-09-22 03:00 |🟥Sell 5880 | BTC | sma60 |114187.81| - |-103.85/53.72| -0.019 | 116350.65 | -0.019 | 119954 | 17302 | /1| -
|
||||
| 2025-10-01 01:00 |🟩Buy 12840 | BTC | bull |114239.53| 4945 | 0/0| - | 114239.53 | - | 119954 | 17302 | 1/1| 4944.8
|
||||
| 2025-10-07 16:00 |🟥Sell 9540 | BTC | B30 |121846.01| 1 |318.99/499.08| 0.067 | 114239.53 | 0.067 | 119954 | 17302 | /1| -
|
||||
2026-02-28 17:34:59,918 - freqtrade.misc - INFO - dumping json to
|
||||
"/freqtrade/user_data/backtest_results/backtest-result-2026-02-28_17-34-59.meta.json"
|
||||
Result for strategy Empty
|
||||
BACKTESTING REPORT
|
||||
┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ BTC/USDT │ 59 │ 3.3 │ 4263.790 │ 426.38 │ 6 days, 23:28:00 │ 33 0 26 55.9 │
|
||||
│ TOTAL │ 59 │ 3.3 │ 4263.790 │ 426.38 │ 6 days, 23:28:00 │ 33 0 26 55.9 │
|
||||
└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
|
||||
LEFT OPEN TRADES REPORT
|
||||
┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ TOTAL │ 0 │ 0.0 │ 0.000 │ 0.0 │ 0:00 │ 0 0 0 0 │
|
||||
└───────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
|
||||
ENTER TAG STATS
|
||||
┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ bull │ 59 │ 3.3 │ 4263.790 │ 426.38 │ 6 days, 23:28:00 │ 33 0 26 55.9 │
|
||||
│ TOTAL │ 59 │ 3.3 │ 4263.790 │ 426.38 │ 6 days, 23:28:00 │ 33 0 26 55.9 │
|
||||
└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
|
||||
EXIT REASON STATS
|
||||
┏━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ B30 │ 33 │ 8.0 │ 6194.610 │ 619.46 │ 11 days, 5:36:00 │ 33 0 0 100 │
|
||||
│ sma60 │ 26 │ -2.67 │ -1930.820 │ -193.08 │ 1 day, 13:51:00 │ 0 0 26 0 │
|
||||
│ TOTAL │ 59 │ 3.3 │ 4263.790 │ 426.38 │ 6 days, 23:28:00 │ 33 0 26 55.9 │
|
||||
└─────────────┴───────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
|
||||
MIXED TAG STATS
|
||||
┏━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ bull │ B30 │ 33 │ 8.0 │ 6194.610 │ 619.46 │ 11 days, 5:36:00 │ 33 0 0 100 │
|
||||
│ bull │ sma60 │ 26 │ -2.67 │ -1930.820 │ -193.08 │ 1 day, 13:51:00 │ 0 0 26 0 │
|
||||
│ TOTAL │ │ 59 │ 3.3 │ 4263.790 │ 426.38 │ 6 days, 23:28:00 │ 33 0 26 55.9 │
|
||||
└───────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
|
||||
MONTH BREAKDOWN
|
||||
┏━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Month ┃ Trades ┃ Tot Profit USDT ┃ Profit Factor ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ 28/02/2023 │ 3 │ 372.897 │ 0.0 │ 3 0 0 100 │
|
||||
│ 31/03/2023 │ 3 │ -29.085 │ 0.79 │ 1 0 2 33.3 │
|
||||
│ 30/04/2023 │ 3 │ 11.898 │ 1.4 │ 2 0 1 66.7 │
|
||||
│ 31/05/2023 │ 2 │ -79.919 │ 0.0 │ 0 0 2 0 │
|
||||
│ 30/06/2023 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||
│ 31/07/2023 │ 2 │ 109.847 │ 3.35 │ 1 0 1 50.0 │
|
||||
│ 31/08/2023 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||
│ 30/09/2023 │ 1 │ -29.039 │ 0.0 │ 0 0 1 0 │
|
||||
│ 31/10/2023 │ 2 │ 70.87 │ 0.0 │ 2 0 0 100 │
|
||||
│ 30/11/2023 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||
│ 31/12/2023 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||
│ 31/01/2024 │ 2 │ 697.801 │ 0.0 │ 2 0 0 100 │
|
||||
│ 29/02/2024 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||
│ 31/03/2024 │ 2 │ 1080.605 │ 0.0 │ 2 0 0 100 │
|
||||
│ 30/04/2024 │ 4 │ -314.088 │ 0.0 │ 0 0 4 0 │
|
||||
│ 31/05/2024 │ 4 │ -182.279 │ 0.09 │ 1 0 3 25.0 │
|
||||
│ 30/06/2024 │ 1 │ -67.429 │ 0.0 │ 0 0 1 0 │
|
||||
│ 31/07/2024 │ 3 │ 111.09 │ 2.87 │ 2 0 1 66.7 │
|
||||
│ 31/08/2024 │ 1 │ -62.063 │ 0.0 │ 0 0 1 0 │
|
||||
│ 30/09/2024 │ 3 │ -88.603 │ 0.28 │ 1 0 2 33.3 │
|
||||
│ 31/10/2024 │ 2 │ 263.26 │ 0.0 │ 2 0 0 100 │
|
||||
│ 30/11/2024 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||
│ 31/12/2024 │ 2 │ 830.987 │ 6.74 │ 1 0 1 50.0 │
|
||||
│ 31/01/2025 │ 5 │ 81.667 │ 1.44 │ 3 0 2 60.0 │
|
||||
│ 28/02/2025 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||
│ 31/03/2025 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||
│ 30/04/2025 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||
│ 31/05/2025 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||
│ 30/06/2025 │ 3 │ 939.688 │ 0.0 │ 3 0 0 100 │
|
||||
│ 31/07/2025 │ 3 │ 138.029 │ 1.57 │ 1 0 2 33.3 │
|
||||
│ 31/08/2025 │ 2 │ 119.103 │ 0.0 │ 2 0 0 100 │
|
||||
│ 30/09/2025 │ 5 │ -30.436 │ 0.85 │ 3 0 2 60.0 │
|
||||
│ 31/10/2025 │ 1 │ 318.991 │ 0.0 │ 1 0 0 100 │
|
||||
└────────────┴────────┴─────────────────┴───────────────┴────────────────────────┘
|
||||
SUMMARY METRICS
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Metric ┃ Value ┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ Backtesting from │ 2023-01-01 00:00:00 │
|
||||
│ Backtesting to │ 2025-10-11 00:00:00 │
|
||||
│ Trading Mode │ Spot │
|
||||
│ Max open trades │ 1 │
|
||||
│ │ │
|
||||
│ Total/Daily Avg Trades │ 59 / 0.06 │
|
||||
│ Starting balance │ 1000 USDT │
|
||||
│ Final balance │ 5263.79 USDT │
|
||||
│ Absolute profit │ 4263.79 USDT │
|
||||
│ Total profit % │ 426.38% │
|
||||
│ CAGR % │ 81.82% │
|
||||
│ Sortino │ 2.82 │
|
||||
│ Sharpe │ 0.35 │
|
||||
│ Calmar │ 42.68 │
|
||||
│ SQN │ 2.37 │
|
||||
│ Profit factor │ 3.21 │
|
||||
│ Expectancy (Ratio) │ 72.27 (0.97) │
|
||||
│ Avg. daily profit │ 4.205 USDT │
|
||||
│ Avg. stake amount │ 2946.519 USDT │
|
||||
│ Total trade volume │ 352657.589 USDT │
|
||||
│ │ │
|
||||
│ Best Pair │ BTC/USDT 426.38% │
|
||||
│ Worst Pair │ BTC/USDT 426.38% │
|
||||
│ Best trade │ BTC/USDT 46.39% │
|
||||
│ Worst trade │ BTC/USDT -5.76% │
|
||||
│ Best day │ 975.709 USDT │
|
||||
│ Worst day │ -144.722 USDT │
|
||||
│ Days win/draw/lose │ 32 / 914 / 26 │
|
||||
│ Min/Max/Avg. Duration Winners │ 1d 09:00 / 87d 12:00 / 11d 05:36 │
|
||||
│ Min/Max/Avg. Duration Losers │ 0d 02:00 / 5d 05:00 / 1d 13:51 │
|
||||
│ Max Consecutive Wins / Loss │ 6 / 6 │
|
||||
│ Rejected Entry signals │ 0 │
|
||||
│ Entry/Exit Timeouts │ 0 / 0 │
|
||||
│ │ │
|
||||
│ Min balance │ 1260.898 USDT │
|
||||
│ Max balance │ 5263.79 USDT │
|
||||
│ Max % of account underwater │ 18.82% │
|
||||
│ Absolute drawdown │ 603.373 USDT (18.82%) │
|
||||
│ Drawdown duration │ 186 days 10:00:00 │
|
||||
│ Profit at drawdown start │ 2205.874 USDT │
|
||||
│ Profit at drawdown end │ 1602.501 USDT │
|
||||
│ Drawdown start │ 2024-03-27 16:00:00 │
|
||||
│ Drawdown end │ 2024-09-30 02:00:00 │
|
||||
│ Market change │ 580.68% │
|
||||
└───────────────────────────────┴──────────────────────────────────┘
|
||||
|
||||
Backtested 2023-01-01 00:00:00 -> 2025-10-11 00:00:00 | Max open trades : 1
|
||||
STRATEGY SUMMARY
|
||||
┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃
|
||||
┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ Empty │ 59 │ 3.30 │ 4263.790 │ 426.38 │ 6 days, 23:28:00 │ 33 0 26 │ 603.373 USDT 18.82% │
|
||||
│ │ │ │ │ │ │ 55.9 │ │
|
||||
└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴───────────────────────┴──────────────────────┘
|
||||
946
Frictrade.py
Normal file
946
Frictrade.py
Normal file
@@ -0,0 +1,946 @@
|
||||
# Zeus Strategy: First Generation of GodStra Strategy with maximum
|
||||
# AVG/MID profit in USDT
|
||||
# Author: @Mablue (Masoud Azizi)
|
||||
# github: https://github.com/mablue/
|
||||
# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
|
||||
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus
|
||||
# --- Do not remove these libs ---
|
||||
from datetime import timedelta, datetime
|
||||
from freqtrade.persistence import Trade
|
||||
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
|
||||
IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import json
|
||||
import csv
|
||||
from pandas import DataFrame
|
||||
from typing import Optional, Union, Tuple
|
||||
import math
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
# --------------------------------
|
||||
|
||||
# Add your lib to import here test git
|
||||
import ta
|
||||
import talib.abstract as talib
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
from datetime import timezone, timedelta
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Couleurs ANSI de base
|
||||
RED = "\033[31m"
|
||||
GREEN = "\033[32m"
|
||||
YELLOW = "\033[33m"
|
||||
BLUE = "\033[34m"
|
||||
MAGENTA = "\033[35m"
|
||||
CYAN = "\033[36m"
|
||||
RESET = "\033[0m"
|
||||
|
||||
|
||||
class Frictrade(IStrategy):
|
||||
startup_candle_count = 180
|
||||
|
||||
# ROI table:
|
||||
minimal_roi = {
|
||||
"0": 10
|
||||
}
|
||||
|
||||
# Stoploss:
|
||||
stoploss = -1 # 0.256
|
||||
# Custom stoploss
|
||||
use_custom_stoploss = False
|
||||
|
||||
trailing_stop = False
|
||||
trailing_stop_positive = 0.15
|
||||
trailing_stop_positive_offset = 0.5
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
# Buy hypers
|
||||
timeframe = '1m'
|
||||
max_open_trades = 5
|
||||
max_amount = 40
|
||||
|
||||
parameters = {}
|
||||
# DCA config
|
||||
position_adjustment_enable = True
|
||||
|
||||
columns_logged = False
|
||||
pairs = {
|
||||
pair: {
|
||||
"first_buy": 0,
|
||||
"last_buy": 0.0,
|
||||
"last_min": 999999999999999.5,
|
||||
"last_max": 0,
|
||||
"trade_info": {},
|
||||
"max_touch": 0.0,
|
||||
"last_sell": 0.0,
|
||||
'count_of_buys': 0,
|
||||
'current_profit': 0,
|
||||
'expected_profit': 0,
|
||||
'previous_profit': 0,
|
||||
"last_candle": {},
|
||||
"last_count_of_buys": 0,
|
||||
'base_stake_amount': 0,
|
||||
'stop_buy': False,
|
||||
'last_date': 0,
|
||||
'stop': False,
|
||||
'max_profit': 0,
|
||||
'first_amount': 0,
|
||||
'total_amount': 0,
|
||||
'has_gain': 0,
|
||||
'force_sell': False,
|
||||
'force_buy': False
|
||||
}
|
||||
for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC",
|
||||
"BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"]
|
||||
}
|
||||
trades = list()
|
||||
max_profit_pairs = {}
|
||||
|
||||
btc_ath_history = [
|
||||
{"date": "2011-06-09", "price_usd": 26.15, "note": "pic 2011 (early breakout)"},
|
||||
{"date": "2013-11-29", "price_usd": 1132.00, "note": "bull run fin 2013"},
|
||||
{"date": "2017-12-17", "price_usd": 19783.00, "note": "ATH décembre 2017 (crypto bubble)"},
|
||||
{"date": "2020-12-31", "price_usd": 29001.72, "note": "fin 2020, nouveau record après accumulation)"},
|
||||
{"date": "2021-11-10", "price_usd": 68742.00, "note": "record novembre 2021 (institutional demand)"},
|
||||
{"date": "2024-03-05", "price_usd": 69000.00,
|
||||
"note": "nouveau pic début 2024 (source presse, valeur indicative)"},
|
||||
{"date": "2025-07-11", "price_usd": 118755.00, "note": "pic juillet 2025 (valeur rapportée par la presse)"},
|
||||
{"date": "2025-10-06", "price_usd": 126198.07,
|
||||
"note": "pic oct. 2025 (source agrégée, à vérifier selon l'exchange)"}
|
||||
]
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
|
||||
current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
|
||||
|
||||
minutes = 0
|
||||
if self.pairs[pair]['last_date'] != 0:
|
||||
minutes = round(int((current_time - self.pairs[pair]['last_date']).total_seconds() / 60))
|
||||
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
last_candle_2 = dataframe.iloc[-2].squeeze()
|
||||
last_candle_3 = dataframe.iloc[-3].squeeze()
|
||||
|
||||
condition = True #(last_candle[f"{indic_5m}_deriv1"] >= indic_deriv1_5m) and (last_candle[f"{indic_5m}_deriv2"] >= indic_deriv2_5m)
|
||||
|
||||
allow_to_buy = True #(condition and not self.pairs[pair]['stop']) | (entry_tag == 'force_entry')
|
||||
|
||||
if allow_to_buy:
|
||||
self.trades = list()
|
||||
self.pairs[pair]['first_buy'] = rate
|
||||
self.pairs[pair]['last_buy'] = rate
|
||||
self.pairs[pair]['max_touch'] = last_candle['close']
|
||||
self.pairs[pair]['last_candle'] = last_candle
|
||||
self.pairs[pair]['count_of_buys'] = 1
|
||||
self.pairs[pair]['current_profit'] = 0
|
||||
self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
|
||||
self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
|
||||
|
||||
dispo = round(self.wallets.get_available_stake_amount())
|
||||
self.printLineLog()
|
||||
|
||||
stake_amount = self.adjust_stake_amount(pair, last_candle)
|
||||
|
||||
self.pairs[pair]['total_amount'] = stake_amount
|
||||
|
||||
self.log_trade(
|
||||
last_candle=last_candle,
|
||||
date=current_time,
|
||||
action=("🟩Buy" if allow_to_buy else "Canceled") + " " + str(minutes),
|
||||
pair=pair,
|
||||
rate=rate,
|
||||
dispo=dispo,
|
||||
profit=0,
|
||||
trade_type=entry_tag,
|
||||
buys=1,
|
||||
stake=round(stake_amount, 2)
|
||||
)
|
||||
|
||||
return allow_to_buy
|
||||
|
||||
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str,
|
||||
exit_reason: str, current_time, **kwargs, ) -> bool:
|
||||
|
||||
# allow_to_sell = (minutes > 30)
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
|
||||
minutes = int(round((current_time - trade.open_date_utc).seconds / 60, 0))
|
||||
profit =trade.calc_profit(rate)
|
||||
force = self.pairs[pair]['force_sell']
|
||||
allow_to_sell = minutes > 30 and (last_candle['hapercent'] < 0 ) or force or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss')
|
||||
|
||||
if allow_to_sell:
|
||||
self.trades = list()
|
||||
self.pairs[pair]['last_count_of_buys'] = trade.nr_of_successful_entries # self.pairs[pair]['count_of_buys']
|
||||
self.pairs[pair]['last_sell'] = rate
|
||||
self.pairs[pair]['last_candle'] = last_candle
|
||||
self.pairs[pair]['previous_profit'] = 0
|
||||
self.trades = list()
|
||||
dispo = round(self.wallets.get_available_stake_amount())
|
||||
# print(f"Sell {pair} {current_time} {exit_reason} dispo={dispo} amount={amount} rate={rate} open_rate={trade.open_rate}")
|
||||
self.log_trade(
|
||||
last_candle=last_candle,
|
||||
date=current_time,
|
||||
action="🟥Sell " + str(minutes),
|
||||
pair=pair,
|
||||
trade_type=exit_reason,
|
||||
rate=last_candle['close'],
|
||||
dispo=dispo,
|
||||
profit=round(profit, 2)
|
||||
)
|
||||
self.pairs[pair]['max_profit'] = 0
|
||||
self.pairs[pair]['force_sell'] = False
|
||||
self.pairs[pair]['has_gain'] = 0
|
||||
self.pairs[pair]['current_profit'] = 0
|
||||
self.pairs[pair]['total_amount'] = 0
|
||||
self.pairs[pair]['count_of_buys'] = 0
|
||||
self.pairs[pair]['max_touch'] = 0
|
||||
self.pairs[pair]['last_buy'] = 0
|
||||
self.pairs[pair]['last_date'] = current_time
|
||||
self.pairs[pair]['current_trade'] = None
|
||||
# else:
|
||||
# self.printLog(f"{current_time} SELL triggered for {pair} ({exit_reason} profit={profit} minutes={minutes} percent={last_candle['hapercent']}) but condition blocked")
|
||||
return (allow_to_sell) | (exit_reason == 'force_exit') | (exit_reason == 'stop_loss')
|
||||
|
||||
# def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs):
|
||||
#
|
||||
# dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
# last_candle = dataframe.iloc[-1].squeeze()
|
||||
# last_candle_1h = dataframe.iloc[-13].squeeze()
|
||||
# before_last_candle = dataframe.iloc[-2].squeeze()
|
||||
# before_last_candle_2 = dataframe.iloc[-3].squeeze()
|
||||
# before_last_candle_12 = dataframe.iloc[-13].squeeze()
|
||||
#
|
||||
# expected_profit = self.expectedProfit(pair, last_candle)
|
||||
# # print(f"current_time={current_time} current_profit={current_profit} expected_profit={expected_profit}")
|
||||
#
|
||||
# max_touch_before = self.pairs[pair]['max_touch']
|
||||
# self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
|
||||
# self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
|
||||
# self.pairs[pair]['current_trade'] = trade
|
||||
#
|
||||
# count_of_buys = trade.nr_of_successful_entries
|
||||
#
|
||||
# profit = trade.calc_profit(current_rate) #round(current_profit * trade.stake_amount, 1)
|
||||
# self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], profit)
|
||||
# max_profit = last_candle['max5'] #self.pairs[pair]['max_profit']
|
||||
# baisse = 0
|
||||
# if profit > 0:
|
||||
# baisse = 1 - (profit / max_profit)
|
||||
# mx = max_profit / 5
|
||||
# self.pairs[pair]['count_of_buys'] = count_of_buys
|
||||
# self.pairs[pair]['current_profit'] = profit
|
||||
#
|
||||
# dispo = round(self.wallets.get_available_stake_amount())
|
||||
# hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
|
||||
# days_since_first_buy = (current_time - trade.open_date_utc).days
|
||||
# hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
||||
# minutes = (current_time - trade.date_last_filled_utc).total_seconds() / 60.0
|
||||
#
|
||||
# if minutes % 4 == 0:
|
||||
# self.log_trade(
|
||||
# last_candle=last_candle,
|
||||
# date=current_time,
|
||||
# action="🟢 CURRENT", #🔴 CURRENT" if self.pairs[pair]['stop'] or last_candle['stop_buying'] else "
|
||||
# dispo=dispo,
|
||||
# pair=pair,
|
||||
# rate=last_candle['close'],
|
||||
# trade_type='',
|
||||
# profit=round(profit, 2),
|
||||
# buys=count_of_buys,
|
||||
# stake=0
|
||||
# )
|
||||
#
|
||||
# if (last_candle['close'] > last_candle['mid']) or (last_candle['sma5_deriv1'] > 0):
|
||||
# return None
|
||||
#
|
||||
# pair_name = self.getShortName(pair)
|
||||
#
|
||||
# if profit > 0.003 * count_of_buys and baisse > 0.30:
|
||||
# self.pairs[pair]['force_sell'] = False
|
||||
# self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
|
||||
# return str(count_of_buys) + '_' + 'B30_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
|
||||
#
|
||||
# self.pairs[pair]['max_touch'] = max(last_candle['close'], self.pairs[pair]['max_touch'])
|
||||
|
||||
def getShortName(self, pair):
|
||||
return pair.replace("/USDT", '').replace("/USDC", '').replace("_USDC", '').replace("_USDT", '')
|
||||
|
||||
def getLastLost(self, last_candle, pair):
|
||||
last_lost = round((last_candle['close'] - self.pairs[pair]['max_touch']) / self.pairs[pair]['max_touch'], 3)
|
||||
return last_lost
|
||||
def getPctFirstBuy(self, pair, last_candle):
|
||||
return round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
|
||||
|
||||
def getPctLastBuy(self, pair, last_candle):
|
||||
return round((last_candle['close'] - self.pairs[pair]['last_buy']) / self.pairs[pair]['last_buy'], 4)
|
||||
|
||||
def expectedProfit(self, pair: str, last_candle: DataFrame):
|
||||
lim = 0.01
|
||||
pct = 0.002
|
||||
if (self.getShortName(pair) == 'BTC'):
|
||||
lim = 0.005
|
||||
pct = 0.001
|
||||
pct_to_max = lim + pct * self.pairs[pair]['count_of_buys']
|
||||
expected_profit = lim * self.pairs[pair]['total_amount'] # min(3 * lim, max(lim, pct_to_max)) # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #min(0.01, first_max)
|
||||
|
||||
self.pairs[pair]['expected_profit'] = expected_profit
|
||||
|
||||
return expected_profit
|
||||
|
||||
def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None,
|
||||
last_candle=None):
|
||||
# Afficher les colonnes une seule fois
|
||||
if self.config.get('runmode') == 'hyperopt' or self.dp.runmode.value in ('hyperopt'):
|
||||
return
|
||||
if self.columns_logged % 10 == 0:
|
||||
self.printLog(
|
||||
f"| {'Date':<16} | {'Action':<10} |{'Pair':<5}| {'Trade Type':<18} |{'Rate':>8} | {'Dispo':>6} | {'Profit':>8} "
|
||||
f"| {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}| {'last_min':>7}|{'Buys':>5}| {'Stake':>5} |"
|
||||
f"{'rsi':>6}" #|Distmax|s201d|s5_1d|s5_2d|s51h|s52h|smt1h|smt2h|tdc1d|tdc1h"
|
||||
)
|
||||
self.printLineLog()
|
||||
df = pd.DataFrame.from_dict(self.pairs, orient='index')
|
||||
colonnes_a_exclure = ['last_candle',
|
||||
'trade_info', 'last_date', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
|
||||
df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
|
||||
# df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
|
||||
|
||||
self.printLog(df_filtered)
|
||||
|
||||
self.columns_logged += 1
|
||||
date = str(date)[:16] if date else "-"
|
||||
limit = None
|
||||
rsi = ''
|
||||
rsi_pct = ''
|
||||
sma5_1d = ''
|
||||
sma5_1h = ''
|
||||
|
||||
sma5 = str(sma5_1d) + ' ' + str(sma5_1h)
|
||||
|
||||
last_lost = self.getLastLost(last_candle, pair)
|
||||
|
||||
if buys is None:
|
||||
buys = ''
|
||||
|
||||
max_touch = ''
|
||||
pct_max = self.getPctFirstBuy(pair, last_candle)
|
||||
|
||||
total_counts = str(buys) + '/' + str(sum(pair_data['count_of_buys'] for pair_data in self.pairs.values()))
|
||||
|
||||
dist_max = ''
|
||||
|
||||
last_max = int(self.pairs[pair]['last_max']) if self.pairs[pair]['last_max'] > 1 else round(
|
||||
self.pairs[pair]['last_max'], 3)
|
||||
last_min = int(self.pairs[pair]['last_min']) if self.pairs[pair]['last_min'] > 1 else round(
|
||||
self.pairs[pair]['last_min'], 3)
|
||||
|
||||
color = GREEN if profit > 0 else RED
|
||||
|
||||
profit = str(profit) + '/' + str(round(self.pairs[pair]['max_profit'], 2))
|
||||
|
||||
# 🟢 Dérivée 1 > 0 et dérivée 2 > 0: tendance haussière qui s’accélère.
|
||||
# 🟡 Dérivée 1 > 0 et dérivée 2 < 0: tendance haussière qui ralentit → essoufflement potentiel.
|
||||
# 🔴 Dérivée 1 < 0 et dérivée 2 < 0: tendance baissière qui s’accélère.
|
||||
# 🟠 Dérivée 1 < 0 et dérivée 2 > 0: tendance baissière qui ralentit → possible bottom.
|
||||
self.printLog(
|
||||
f"| {date:<16} |{action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} |{rate or '-':>9}| {dispo or '-':>6} "
|
||||
f"|{color}{profit or '-':>10}{RESET}| {pct_max or '-':>6} | {round(self.pairs[pair]['max_touch'], 2) or '-':>11} | {last_lost or '-':>12} "
|
||||
f"| {last_max or '-':>7} | {last_min or '-':>7} |{total_counts or '-':>5}|{stake or '-':>7}"
|
||||
f"{round(last_candle['max_rsi_24'], 1) or '-' :>6}|{round(last_candle['rsi_1h'], 1) or '-' :>6}|{round(last_candle['rsi_1d'], 1) or '-' :>6}|"
|
||||
f"{round(last_candle['rtp_1h'] * 100, 0) or '-' :>6}|{round(last_candle['rtp_1d'] * 100, 0) or '-' :>6}|"
|
||||
|
||||
)
|
||||
|
||||
def printLineLog(self):
|
||||
# f"sum1h|sum1d|Tdc|Tdh|Tdd| drv1 |drv|drv_1d|"
|
||||
self.printLog(
|
||||
f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 12}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}{'-' * 9}+{'-' * 5}+{'-' * 7}+"
|
||||
f"+{'-' * 6}+{'-' * 7}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+"
|
||||
)
|
||||
|
||||
def printLog(self, str):
|
||||
if self.config.get('runmode') == 'hyperopt' or self.dp.runmode.value in ('hyperopt'):
|
||||
return;
|
||||
if not self.dp.runmode.value in ('backtest', 'hyperopt', 'lookahead-analysis'):
|
||||
logger.info(str)
|
||||
else:
|
||||
if not self.dp.runmode.value in ('hyperopt'):
|
||||
print(str)
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# Add all ta features
|
||||
pair = metadata['pair']
|
||||
short_pair = self.getShortName(pair)
|
||||
self.path = f"user_data/plots/{short_pair}/" + ("valide/" if not self.dp.runmode.value in ('backtest') else '')
|
||||
|
||||
heikinashi = qtpylib.heikinashi(dataframe)
|
||||
dataframe['haopen'] = heikinashi['open']
|
||||
dataframe['haclose'] = heikinashi['close']
|
||||
dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
|
||||
|
||||
dataframe['mid'] = dataframe['open'] + (dataframe['close'] - dataframe['open']) / 2
|
||||
dataframe['sma5'] = dataframe['mid'].ewm(span=5, adjust=False).mean() #dataframe["mid"].rolling(window=5).mean()
|
||||
dataframe['sma5_deriv1'] = 1000 * (dataframe['sma5'] - dataframe['sma5'].shift(1)) / dataframe['sma5'].shift(1)
|
||||
|
||||
dataframe['sma24'] = dataframe['mid'].ewm(span=24, adjust=False).mean()
|
||||
dataframe['sma24_deriv1'] = 1000 * (dataframe['sma24'] - dataframe['sma24'].shift(1)) / dataframe['sma24'].shift(1)
|
||||
|
||||
dataframe['sma60'] = dataframe['mid'].ewm(span=60, adjust=False).mean()
|
||||
dataframe['sma60_deriv1'] = 1000 * (dataframe['sma60'] - dataframe['sma60'].shift(1)) / dataframe['sma60'].shift(1)
|
||||
|
||||
# dataframe[f"sma5_inv"] = (dataframe[f"sma5"].shift(2) >= dataframe[f"sma5"].shift(1)) \
|
||||
# & (dataframe[f"sma5"].shift(1) <= dataframe[f"sma5"])
|
||||
|
||||
dataframe["sma5_sqrt"] = (
|
||||
np.sqrt(np.abs(dataframe["sma5"] - dataframe["sma5"].shift(1)))
|
||||
+ np.sqrt(np.abs(dataframe["sma5"].shift(3) - dataframe["sma5"].shift(1)))
|
||||
)
|
||||
dataframe["sma5_inv"] = (
|
||||
(dataframe["sma5"].shift(2) >= dataframe["sma5"].shift(1))
|
||||
& (dataframe["sma5"].shift(1) <= dataframe["sma5"])
|
||||
& (dataframe["sma5_sqrt"] > 5)
|
||||
)
|
||||
|
||||
dataframe["percent"] = dataframe['mid'].pct_change()
|
||||
dataframe["percent3"] = dataframe['mid'].pct_change(3).rolling(3).mean()
|
||||
dataframe["percent12"] = dataframe['mid'].pct_change(12).rolling(12).mean()
|
||||
dataframe["percent24"] = dataframe['mid'].pct_change(24).rolling(24).mean()
|
||||
|
||||
dataframe['rsi'] = talib.RSI(dataframe['mid'], timeperiod=14)
|
||||
dataframe['max_rsi_12'] = talib.MAX(dataframe['rsi'], timeperiod=12)
|
||||
dataframe['max_rsi_24'] = talib.MAX(dataframe['rsi'], timeperiod=24)
|
||||
dataframe['max5'] = talib.MAX(dataframe['mid'], timeperiod=5)
|
||||
dataframe['min180'] = talib.MIN(dataframe['mid'], timeperiod=180)
|
||||
dataframe['max180'] = talib.MAX(dataframe['mid'], timeperiod=180)
|
||||
dataframe['pct180'] = ((dataframe["mid"] - dataframe['min180'] ) / (dataframe['max180'] - dataframe['min180'] ))
|
||||
|
||||
dataframe = self.rsi_trend_probability(dataframe, short=60, long=360)
|
||||
|
||||
# ################### INFORMATIVE 1h
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
|
||||
informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
|
||||
# informative = self.populate1hIndicators(df=informative, metadata=metadata)
|
||||
informative['rsi'] = talib.RSI(informative['mid'], timeperiod=14)
|
||||
informative = self.rsi_trend_probability(informative)
|
||||
# informative = self.calculateRegression(informative, 'mid', lookback=15)
|
||||
dataframe = merge_informative_pair(dataframe, informative, '1m', '1h', ffill=True)
|
||||
|
||||
# ################### INFORMATIVE 1d
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
|
||||
informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
|
||||
informative['rsi'] = talib.RSI(informative['mid'], timeperiod=5)
|
||||
informative = self.rsi_trend_probability(informative)
|
||||
# informative = self.calculateRegression(informative, 'mid', lookback=15)
|
||||
dataframe = merge_informative_pair(dataframe, informative, '1m', '1d', ffill=True)
|
||||
|
||||
dataframe['last_price'] = dataframe['close']
|
||||
dataframe['first_price'] = dataframe['close']
|
||||
if self.dp:
|
||||
if self.dp.runmode.value in ('live', 'dry_run'):
|
||||
self.getOpenTrades()
|
||||
|
||||
for trade in self.trades:
|
||||
if trade.pair != pair:
|
||||
continue
|
||||
filled_buys = trade.select_filled_orders('buy')
|
||||
count = 0
|
||||
amount = 0
|
||||
for buy in filled_buys:
|
||||
if count == 0:
|
||||
dataframe['first_price'] = buy.price
|
||||
self.pairs[pair]['first_buy'] = buy.price
|
||||
self.pairs[pair]['first_amount'] = buy.price * buy.filled
|
||||
# dataframe['close01'] = buy.price * 1.01
|
||||
|
||||
# Order(id=2396, trade=1019, order_id=29870026652, side=buy, filled=0.00078, price=63921.01,
|
||||
# status=closed, date=2024-08-26 02:20:11)
|
||||
dataframe['last_price'] = buy.price
|
||||
self.pairs[pair]['last_buy'] = buy.price
|
||||
count = count + 1
|
||||
amount += buy.price * buy.filled
|
||||
count_buys = count
|
||||
self.pairs[pair]['total_amount'] = amount
|
||||
|
||||
dataframe['absolute_min'] = dataframe['mid'].rolling(1440, min_periods=1).min()
|
||||
dataframe['absolute_max'] = dataframe['mid'].rolling(1440, min_periods=1).max()
|
||||
# steps = (dataframe['absolute_max'] - dataframe['absolute_min']) / (dataframe['absolute_min'] * 0.01)
|
||||
# levels = [dataframe['absolute_min'] * (1 + i / 100) for i in range(1, steps + 1)]
|
||||
#
|
||||
# print(levels)
|
||||
|
||||
# print(f"min={dataframe['absolute_min'].min()} max={dataframe['absolute_max'].max()}")
|
||||
for i in [0, 1, 2, 3]:
|
||||
dataframe[f"lvl_{i}_pct"] = dataframe['absolute_min'] * (1 + 0.01 * i)
|
||||
|
||||
#
|
||||
# absolute_min = dataframe['absolute_min'].min()
|
||||
# absolute_max = dataframe['absolute_max'].max()
|
||||
#
|
||||
# # Écart total
|
||||
# diff = absolute_max - absolute_min
|
||||
#
|
||||
# # Nombre de lignes intermédiaires (1% steps)
|
||||
# steps = int((absolute_max - absolute_min) / (absolute_min * 0.01))
|
||||
#
|
||||
# # Niveaux de prix à 1%, 2%, ..., steps%
|
||||
# levels = [absolute_min * (1 + i / 100) for i in range(1, steps + 1)]
|
||||
# levels = [lvl for lvl in levels if lvl < absolute_max] # évite le dernier niveau exact
|
||||
#
|
||||
# # ajout dans le DataFrame
|
||||
# for i, lvl in enumerate(levels, start=1):
|
||||
# dataframe[f"lvl_{i}_pct"] = lvl
|
||||
|
||||
# # Indices correspondants
|
||||
# indices = [(dataframe['mid'] - lvl).abs().idxmin() for lvl in levels]
|
||||
|
||||
return dataframe
|
||||
|
||||
def getOpenTrades(self):
|
||||
# if len(self.trades) == 0:
|
||||
self.trades = Trade.get_open_trades()
|
||||
return self.trades
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe.loc[
|
||||
(
|
||||
# (dataframe['sma5_inv'] == 1)
|
||||
# (
|
||||
# ((dataframe['pct180'] < 0.5) | (dataframe['sma24_deriv1'] > 0))
|
||||
# |((dataframe['close'] < dataframe['sma24'] ) & (dataframe['sma24_deriv1'] > 0))
|
||||
#
|
||||
# )
|
||||
(dataframe['hapercent'] > 0)
|
||||
& (dataframe['rsi'] < 85)
|
||||
& (dataframe['sma24'] > dataframe['sma60'])
|
||||
# & (
|
||||
# (dataframe['percent3'] <= -0.003)
|
||||
# | (dataframe['percent12'] <= -0.003)
|
||||
# | (dataframe['percent24'] <= -0.003)
|
||||
# )
|
||||
), ['enter_long', 'enter_tag']] = (1, f"future")
|
||||
|
||||
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.003, np.nan)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
return dataframe
|
||||
|
||||
# def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
|
||||
# # Calculer le minimum des 14 derniers jours
|
||||
# nb_pairs = len(self.dp.current_whitelist())
|
||||
#
|
||||
# base_stake_amount = self.config.get('stake_amount')
|
||||
#
|
||||
# if True : #self.pairs[pair]['count_of_buys'] == 0:
|
||||
# factor = 1 #65 / min(65, last_candle['rsi_1d'])
|
||||
# # if last_candle['min_max_60'] > 0.04:
|
||||
# # factor = 2
|
||||
#
|
||||
# adjusted_stake_amount = base_stake_amount #max(base_stake_amount / 5, base_stake_amount * factor)
|
||||
# else:
|
||||
# adjusted_stake_amount = self.pairs[pair]['first_amount']
|
||||
#
|
||||
# if self.pairs[pair]['count_of_buys'] == 0:
|
||||
# self.pairs[pair]['first_amount'] = adjusted_stake_amount
|
||||
#
|
||||
# return adjusted_stake_amount
|
||||
|
||||
def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
|
||||
|
||||
ath = max(self.pairs[pair]['last_max'], self.get_last_ath_before_candle(last_candle))
|
||||
|
||||
ath_dist = 100 * (ath - last_candle["mid"]) / ath
|
||||
|
||||
# ath_dist
|
||||
# 0 ==> 1
|
||||
# 20 ==> 1.5
|
||||
# 40 ==> 2
|
||||
# 50 * (1 + (ath_dist / 40))
|
||||
base_stake = self.config.get('stake_amount') * (1 + (ath_dist / 40))
|
||||
|
||||
# Calcule max/min 180
|
||||
low180 = last_candle["min180"]
|
||||
high180 = last_candle["max180"]
|
||||
|
||||
mult = 1 - ((last_candle["mid"] - low180) / (high180 - low180))
|
||||
|
||||
print(f"low={low180} mid={last_candle['mid']} high={high180} mult={mult} ath={ath} ath_dist={round(ath_dist, 2)}" )
|
||||
# base_size = montant de base que tu veux utiliser (ex: stake_amount ou autre)
|
||||
base_size = base_stake # exemple fraction du portefeuille; adapte selon ton code
|
||||
# new stake proportionnel à mult
|
||||
new_stake = base_size #* mult
|
||||
return new_stake
|
||||
|
||||
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, min_stake: float,
|
||||
max_stake: float, **kwargs):
|
||||
# ne rien faire si ordre deja en cours
|
||||
if trade.has_open_orders:
|
||||
# self.printLog("skip open orders")
|
||||
return None
|
||||
if (self.wallets.get_available_stake_amount() < 10): # or trade.stake_amount >= max_stake:
|
||||
return 0
|
||||
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
before_last_candle = dataframe.iloc[-2].squeeze()
|
||||
# prépare les données
|
||||
current_time = current_time.astimezone(timezone.utc)
|
||||
open_date = trade.open_date.astimezone(timezone.utc)
|
||||
dispo = round(self.wallets.get_available_stake_amount())
|
||||
hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
|
||||
days_since_first_buy = (current_time - trade.open_date_utc).days
|
||||
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
||||
count_of_buys = trade.nr_of_successful_entries
|
||||
current_time_utc = current_time.astimezone(timezone.utc)
|
||||
open_date = trade.open_date.astimezone(timezone.utc)
|
||||
days_since_open = (current_time_utc - open_date).days
|
||||
pair = trade.pair
|
||||
profit = trade.calc_profit(current_rate) #round(current_profit * trade.stake_amount, 1)
|
||||
last_lost = self.getLastLost(last_candle, pair)
|
||||
pct_first = 0
|
||||
|
||||
total_counts = sum(
|
||||
pair_data['count_of_buys'] for pair_data in self.pairs.values() if not self.getShortName(pair) == 'BTC')
|
||||
|
||||
if self.pairs[pair]['first_buy']:
|
||||
pct_first = self.getPctFirstBuy(pair, last_candle)
|
||||
|
||||
lim = 0.3
|
||||
if (len(dataframe) < 1):
|
||||
# self.printLog("skip dataframe")
|
||||
return None
|
||||
|
||||
# Dernier prix d'achat réel (pas le prix moyen)
|
||||
last_fill_price = self.pairs[trade.pair]['last_buy'] #trade.open_rate # remplacé juste après ↓
|
||||
|
||||
# if len(trade.orders) > 0:
|
||||
# # On cherche le dernier BUY exécuté
|
||||
# buy_orders = [o for o in trade.orders if o.is_buy and o.status == "closed"]
|
||||
# if buy_orders:
|
||||
# last_fill_price = buy_orders[-1].price
|
||||
|
||||
# baisse relative
|
||||
dca_threshold = 0.0025 * count_of_buys
|
||||
decline = (last_fill_price - current_rate) / last_fill_price
|
||||
increase = - decline
|
||||
|
||||
# if decline >= self.dca_threshold:
|
||||
# # Exemple : on achète 50% du montant du dernier trade
|
||||
# last_amount = buy_orders[-1].amount if buy_orders else 0
|
||||
# stake_amount = last_amount * current_rate * 0.5
|
||||
# return stake_amount
|
||||
|
||||
condition = last_candle['hapercent'] > 0 and last_candle['sma24_deriv1'] > 0
|
||||
limit_buy = 40
|
||||
# or (last_candle['close'] <= last_candle['min180'] and hours > 3)
|
||||
if (decline >= dca_threshold) and condition:
|
||||
try:
|
||||
if self.pairs[pair]['has_gain'] and profit > 0:
|
||||
self.pairs[pair]['force_sell'] = True
|
||||
self.pairs[pair]['previous_profit'] = profit
|
||||
return None
|
||||
|
||||
max_amount = self.config.get('stake_amount') * 2.5
|
||||
stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
|
||||
self.adjust_stake_amount(pair, last_candle))
|
||||
# print(f"profit={profit} previous={self.pairs[pair]['previous_profit']} count_of_buys={trade.nr_of_successful_entries}")
|
||||
if stake_amount > 0:
|
||||
self.pairs[pair]['previous_profit'] = profit
|
||||
trade_type = "Loss " + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
|
||||
self.pairs[trade.pair]['count_of_buys'] += 1
|
||||
self.pairs[pair]['total_amount'] += stake_amount
|
||||
self.log_trade(
|
||||
last_candle=last_candle,
|
||||
date=current_time,
|
||||
action="🟧 Loss -",
|
||||
dispo=dispo,
|
||||
pair=trade.pair,
|
||||
rate=current_rate,
|
||||
trade_type=trade_type,
|
||||
profit=round(profit, 1),
|
||||
buys=trade.nr_of_successful_entries + 1,
|
||||
stake=round(stake_amount, 2)
|
||||
)
|
||||
|
||||
self.pairs[trade.pair]['last_buy'] = current_rate
|
||||
self.pairs[trade.pair]['max_touch'] = last_candle['close']
|
||||
self.pairs[trade.pair]['last_candle'] = last_candle
|
||||
|
||||
# df = pd.DataFrame.from_dict(self.pairs, orient='index')
|
||||
# colonnes_a_exclure = ['last_candle', 'stop',
|
||||
# 'trade_info', 'last_date', 'expected_profit', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
|
||||
# df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
|
||||
# # df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
|
||||
#
|
||||
# self.printLog(df_filtered)
|
||||
|
||||
return stake_amount
|
||||
return None
|
||||
except Exception as exception:
|
||||
self.printLog(exception)
|
||||
return None
|
||||
|
||||
if current_profit > dca_threshold and (increase >= dca_threshold and self.wallets.get_available_stake_amount() > 0):
|
||||
try:
|
||||
self.pairs[pair]['previous_profit'] = profit
|
||||
stake_amount = max(20, min(self.wallets.get_available_stake_amount(), self.adjust_stake_amount(pair, last_candle)))
|
||||
if stake_amount > 0:
|
||||
self.pairs[pair]['has_gain'] += 1
|
||||
|
||||
trade_type = 'Gain +' + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
|
||||
self.pairs[trade.pair]['count_of_buys'] += 1
|
||||
self.pairs[pair]['total_amount'] += stake_amount
|
||||
self.log_trade(
|
||||
last_candle=last_candle,
|
||||
date=current_time,
|
||||
action="🟡 Gain +",
|
||||
dispo=dispo,
|
||||
pair=trade.pair,
|
||||
rate=current_rate,
|
||||
trade_type='Gain',
|
||||
profit=round(profit, 1),
|
||||
buys=trade.nr_of_successful_entries + 1,
|
||||
stake=round(stake_amount, 2)
|
||||
)
|
||||
self.pairs[trade.pair]['last_buy'] = current_rate
|
||||
self.pairs[trade.pair]['max_touch'] = last_candle['close']
|
||||
self.pairs[trade.pair]['last_candle'] = last_candle
|
||||
return stake_amount
|
||||
return None
|
||||
except Exception as exception:
|
||||
self.printLog(exception)
|
||||
return None
|
||||
|
||||
return None
|
||||
|
||||
def custom_exit(self, pair, trade, current_time, current_rate, current_profit, **kwargs):
|
||||
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
last_candle_1h = dataframe.iloc[-13].squeeze()
|
||||
before_last_candle = dataframe.iloc[-2].squeeze()
|
||||
before_last_candle_2 = dataframe.iloc[-3].squeeze()
|
||||
before_last_candle_12 = dataframe.iloc[-13].squeeze()
|
||||
|
||||
expected_profit = self.expectedProfit(pair, last_candle)
|
||||
# print(f"current_time={current_time} current_profit={current_profit} expected_profit={expected_profit}")
|
||||
|
||||
# ----- 1) Charger les variables de trailing pour ce trade -----
|
||||
max_price = self.pairs[pair]['max_touch']
|
||||
|
||||
self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
|
||||
self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
|
||||
self.pairs[pair]['current_trade'] = trade
|
||||
|
||||
count_of_buys = trade.nr_of_successful_entries
|
||||
|
||||
profit = trade.calc_profit(current_rate) #round(current_profit * trade.stake_amount, 1)
|
||||
|
||||
if current_profit > 0:
|
||||
self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], profit)
|
||||
# else:
|
||||
# self.pairs[pair]['max_profit'] = 0
|
||||
|
||||
max_profit = self.pairs[pair]['max_profit']
|
||||
|
||||
# if current_profit > 0:
|
||||
# print(f"profit={profit} max_profit={max_profit} current_profit={current_profit}")
|
||||
|
||||
baisse = 0
|
||||
if profit > 0:
|
||||
baisse = 1 - (profit / max_profit)
|
||||
mx = max_profit / 5
|
||||
self.pairs[pair]['count_of_buys'] = count_of_buys
|
||||
self.pairs[pair]['current_profit'] = profit
|
||||
|
||||
dispo = round(self.wallets.get_available_stake_amount())
|
||||
hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
|
||||
days_since_first_buy = (current_time - trade.open_date_utc).days
|
||||
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
||||
minutes = (current_time - trade.date_last_filled_utc).total_seconds() / 60.0
|
||||
|
||||
# ----- 2) Mise à jour du max_price -----
|
||||
self.pairs[pair]['max_touch'] = max(last_candle['close'], self.pairs[pair]['max_touch'])
|
||||
|
||||
# ----- 3) Calcul du profit max atteint -----
|
||||
# profit_max = (max_price - trade.open_rate) / trade.open_rate
|
||||
|
||||
current_trailing_stop_positive = self.trailing_stop_positive
|
||||
current_trailing_only_offset_is_reached = self.trailing_only_offset_is_reached
|
||||
current_trailing_stop_positive_offset = self.trailing_stop_positive_offset
|
||||
|
||||
max_ = last_candle['max180']
|
||||
min_ = last_candle['min180']
|
||||
mid = last_candle['mid']
|
||||
# éviter division par zéro
|
||||
position = (mid - min_) / (max_ - min_)
|
||||
zone = int(position * 3) # 0 à 2
|
||||
|
||||
if zone == 0:
|
||||
current_trailing_stop_positive = self.trailing_stop_positive
|
||||
current_trailing_stop_positive_offset = self.trailing_stop_positive_offset * 2
|
||||
if minutes > 1440:
|
||||
current_trailing_only_offset_is_reached = False
|
||||
current_trailing_stop_positive_offset = self.trailing_stop_positive_offset
|
||||
# if zone == 1:
|
||||
|
||||
# ----- 5) Calcul du trailing stop dynamique -----
|
||||
# Exemple : offset=0.321 => stop à +24.8%
|
||||
|
||||
trailing_stop = max_profit * (1.0 - current_trailing_stop_positive)
|
||||
baisse = 0
|
||||
if max_profit:
|
||||
baisse = (max_profit - profit) / max_profit
|
||||
|
||||
if minutes % 12 == 0:
|
||||
self.log_trade(
|
||||
last_candle=last_candle,
|
||||
date=current_time,
|
||||
action="🟢 CURRENT", #🔴 CURRENT" if self.pairs[pair]['stop'] or last_candle['stop_buying'] else "
|
||||
dispo=dispo,
|
||||
pair=pair,
|
||||
rate=last_candle['close'],
|
||||
trade_type=f"{round(profit, 2)} {round(max_profit, 2)} {round(trailing_stop,2)} {minutes}",
|
||||
profit=round(profit, 2),
|
||||
buys=count_of_buys,
|
||||
stake=0
|
||||
)
|
||||
|
||||
# if last_candle['sma24_deriv1'] > 0 : #and minutes < 180 and baisse < 30: # and last_candle['sma5_deriv1'] > -0.15:
|
||||
# if (minutes < 180):
|
||||
# return None
|
||||
# if (minutes > 1440 and last_candle['sma60_deriv1'] > 0) :
|
||||
# return None
|
||||
|
||||
|
||||
# ----- 4) OFFSET : faut-il attendre de dépasser trailing_stop_positive_offset ? -----
|
||||
if current_trailing_only_offset_is_reached:
|
||||
# Max profit pas atteint ET perte < 2 * current_trailing_stop_positive
|
||||
if max_profit < min(2, current_trailing_stop_positive_offset * (count_of_buys - self.pairs[pair]['has_gain']))\
|
||||
and (max_profit > current_trailing_stop_positive_offset): #2 * current_trailing_stop_positive:
|
||||
return None # ne pas activer le trailing encore
|
||||
# Sinon : trailing actif dès le début
|
||||
|
||||
# ----- 6) Condition de vente -----
|
||||
if 0 < profit <= trailing_stop and last_candle['mid'] < last_candle['sma5']:
|
||||
return f"stop_{count_of_buys}_{self.pairs[pair]['has_gain']}"
|
||||
return None
|
||||
|
||||
def informative_pairs(self):
|
||||
# get access to all pairs available in whitelist.
|
||||
pairs = self.dp.current_whitelist()
|
||||
informative_pairs = [(pair, '1h') for pair in pairs]
|
||||
informative_pairs += [(pair, '1d') for pair in pairs]
|
||||
|
||||
return informative_pairs
|
||||
|
||||
def populate1hIndicators(self, df: pd.DataFrame, metadata: dict) -> pd.DataFrame:
|
||||
|
||||
# --- WEEKLY LEVELS ---
|
||||
# semaine précédente = semaine ISO différente
|
||||
df["week"] = df.index.isocalendar().week
|
||||
df["year"] = df.index.year
|
||||
|
||||
df["weekly_low"] = (
|
||||
df.groupby(["year", "week"])["low"]
|
||||
.transform("min")
|
||||
.shift(1) # décalé -> pas regarder la semaine en cours
|
||||
)
|
||||
df["weekly_high"] = (
|
||||
df.groupby(["year", "week"])["high"]
|
||||
.transform("max")
|
||||
.shift(1)
|
||||
)
|
||||
|
||||
# Définition simple d'une zone de demande hebdo :
|
||||
# bas + 25% de la bougie => modifiable
|
||||
df["weekly_demand_zone_low"] = df["weekly_low"]
|
||||
df["weekly_demand_zone_high"] = df["weekly_low"] * 1.025
|
||||
|
||||
# --- MONTHLY LEVELS ---
|
||||
df["month"] = df.index.month
|
||||
|
||||
df["monthly_low"] = (
|
||||
df.groupby(["year", "month"])["low"]
|
||||
.transform("min")
|
||||
.shift(1) # mois précédent uniquement
|
||||
)
|
||||
df["monthly_high"] = (
|
||||
df.groupby(["year", "month"])["high"]
|
||||
.transform("max")
|
||||
.shift(1)
|
||||
)
|
||||
|
||||
df["monthly_demand_zone_low"] = df["monthly_low"]
|
||||
df["monthly_demand_zone_high"] = df["monthly_low"] * 1.03
|
||||
|
||||
return df
|
||||
|
||||
# ----- SIGNALS SIMPLES POUR EXEMPLE -----
|
||||
|
||||
# def populate_buy_trend(self, df: pd.DataFrame, metadata: dict) -> pd.DataFrame:
|
||||
# df["buy"] = 0
|
||||
#
|
||||
# # Exemple : acheter si le prix tape la zone de demande hebdomadaire
|
||||
# df.loc[
|
||||
# (df["close"] <= df["weekly_demand_zone_high"]) &
|
||||
# (df["close"] >= df["weekly_demand_zone_low"]),
|
||||
# "buy"
|
||||
# ] = 1
|
||||
#
|
||||
# return df
|
||||
#
|
||||
# def populate_sell_trend(self, df: pd.DataFrame, metadata: dict) -> pd.DataFrame:
|
||||
# df["sell"] = 0
|
||||
#
|
||||
# # Exemple : vendre sur retour au weekly_high précédent
|
||||
# df.loc[df["close"] >= df["weekly_high"], "sell"] = 1
|
||||
#
|
||||
# return df
|
||||
|
||||
|
||||
def rsi_trend_probability(self, dataframe, short=6, long=12):
|
||||
dataframe = dataframe.copy()
|
||||
|
||||
dataframe['rsi_short'] = talib.RSI(dataframe['mid'], short)
|
||||
dataframe['rsi_long'] = talib.RSI(dataframe['mid'], long)
|
||||
|
||||
dataframe['cross_soft'] = np.tanh((dataframe['rsi_short'] - dataframe['rsi_long']) / 7)
|
||||
|
||||
dataframe['gap'] = (dataframe['rsi_short'] - dataframe['rsi_long']) / 100
|
||||
dataframe['trend'] = (dataframe['rsi_long'] - 50) / 50
|
||||
|
||||
dataframe['rtp'] = (
|
||||
0.6 * dataframe['cross_soft'] +
|
||||
0.25 * dataframe['gap'] +
|
||||
0.15 * dataframe['trend']
|
||||
).clip(-1, 1)
|
||||
|
||||
return dataframe
|
||||
|
||||
import pandas as pd
|
||||
|
||||
def to_utc_ts(self, x):
|
||||
return pd.to_datetime(x, utc=True)
|
||||
|
||||
# suppose self.btc_ath_history exists (liste de dict)
|
||||
def get_last_ath_before_candle(self, last_candle):
|
||||
candle_date = self.to_utc_ts(last_candle['date']) # ou to_utc_ts(last_candle.name)
|
||||
best = None
|
||||
for a in self.btc_ath_history: #getattr(self, "btc_ath_history", []):
|
||||
ath_date = self.to_utc_ts(a["date"])
|
||||
if ath_date <= candle_date:
|
||||
if best is None or ath_date > best[0]:
|
||||
best = (ath_date, a["price_usd"])
|
||||
return best[1] if best is not None else None
|
||||
|
||||
35
FrictradeLearning.json
Normal file
35
FrictradeLearning.json
Normal file
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"strategy_name": "FrictradeLearning",
|
||||
"params": {
|
||||
"roi": {
|
||||
"0": 10
|
||||
},
|
||||
"stoploss": {
|
||||
"stoploss": -1.0
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": false,
|
||||
"trailing_stop_positive": 0.15,
|
||||
"trailing_stop_positive_offset": 1,
|
||||
"trailing_only_offset_is_reached": true
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 20
|
||||
},
|
||||
"protection": {
|
||||
"allow_decrease_rate": 0.4,
|
||||
"first_adjust_param": 0.005,
|
||||
"max_steps": 30
|
||||
},
|
||||
"buy": {
|
||||
"hours_force": 44,
|
||||
"indic_1h_force_buy": "sma5_deriv1_1h"
|
||||
},
|
||||
"sell": {
|
||||
"offset_max": 6,
|
||||
"offset_min": 27
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-15 19:03:21.177158+00:00"
|
||||
}
|
||||
2635
FrictradeLearning.py
Normal file
2635
FrictradeLearning.py
Normal file
File diff suppressed because it is too large
Load Diff
25
Genetic.json
Normal file
25
Genetic.json
Normal file
@@ -0,0 +1,25 @@
|
||||
{
|
||||
"strategy_name": "Genetic",
|
||||
"params": {
|
||||
"roi": {
|
||||
"0": 0.21029,
|
||||
"11": 0.05876,
|
||||
"57": 0.02191,
|
||||
"281": 0
|
||||
},
|
||||
"stoploss": {
|
||||
"stoploss": -1.0
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 80
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.028,
|
||||
"trailing_stop_positive_offset": 0.105,
|
||||
"trailing_only_offset_is_reached": true
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-04 23:58:24.045587+00:00"
|
||||
}
|
||||
@@ -160,7 +160,7 @@ class Genetic(IStrategy):
|
||||
}
|
||||
|
||||
# Stoploss:
|
||||
stoploss = -0.07693
|
||||
stoploss = -1
|
||||
|
||||
# Optimal ticker interval for the strategy
|
||||
ticker_interval = '2h'
|
||||
@@ -359,4 +359,5 @@ class Genetic(IStrategy):
|
||||
|
||||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
return dataframe
|
||||
return dataframe
|
||||
|
||||
|
||||
186
GodStra.py
186
GodStra.py
@@ -11,8 +11,13 @@
|
||||
|
||||
# --- Do not remove these libs ---
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
from datetime import timedelta, datetime
|
||||
from freqtrade.persistence import Trade
|
||||
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
|
||||
IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
|
||||
from pandas import DataFrame
|
||||
# --------------------------------
|
||||
from datetime import timezone, timedelta
|
||||
|
||||
# Add your lib to import here
|
||||
# import talib.abstract as ta
|
||||
@@ -24,6 +29,38 @@ import numpy as np
|
||||
|
||||
|
||||
class GodStra(IStrategy):
|
||||
position_adjustment_enable = True
|
||||
|
||||
columns_logged = False
|
||||
pairs = {
|
||||
pair: {
|
||||
"first_buy": 0,
|
||||
"last_buy": 0.0,
|
||||
"last_min": 999999999999999.5,
|
||||
"last_max": 0,
|
||||
"trade_info": {},
|
||||
"max_touch": 0.0,
|
||||
"last_sell": 0.0,
|
||||
'count_of_buys': 0,
|
||||
'current_profit': 0,
|
||||
'expected_profit': 0,
|
||||
'previous_profit': 0,
|
||||
"last_candle": {},
|
||||
"last_count_of_buys": 0,
|
||||
'base_stake_amount': 0,
|
||||
'stop_buy': False,
|
||||
'last_date': 0,
|
||||
'stop': False,
|
||||
'max_profit': 0,
|
||||
'first_amount': 0,
|
||||
'total_amount': 0,
|
||||
'has_gain': 0,
|
||||
'force_sell': False,
|
||||
'force_buy': False
|
||||
}
|
||||
for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC",
|
||||
"BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"]
|
||||
}
|
||||
# 5/66: 9 trades. 8/0/1 Wins/Draws/Losses. Avg profit 21.83%. Median profit 35.52%. Total profit 1060.11476586 USDT ( 196.50Σ%). Avg duration 3440.0 min. Objective: -7.06960
|
||||
# +--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------+
|
||||
# | Best | Epoch | Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Objective |
|
||||
@@ -67,7 +104,7 @@ class GodStra(IStrategy):
|
||||
trailing_stop_positive_offset = 0.2684
|
||||
trailing_only_offset_is_reached = True
|
||||
# Buy hypers
|
||||
timeframe = '12h'
|
||||
timeframe = '1m'
|
||||
print('Add {\n\t"method": "AgeFilter",\n\t"min_days_listed": 30\n},\n to your pairlists in config (Under StaticPairList)')
|
||||
|
||||
def dna_size(self, dct: dict):
|
||||
@@ -169,3 +206,150 @@ class GodStra(IStrategy):
|
||||
'sell'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, min_stake: float,
|
||||
max_stake: float, **kwargs):
|
||||
# ne rien faire si ordre deja en cours
|
||||
print('ici')
|
||||
if trade.has_open_orders:
|
||||
# self.printLog("skip open orders")
|
||||
return None
|
||||
if (self.wallets.get_available_stake_amount() < 10): # or trade.stake_amount >= max_stake:
|
||||
return 0
|
||||
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
before_last_candle = dataframe.iloc[-2].squeeze()
|
||||
# prépare les données
|
||||
current_time = current_time.astimezone(timezone.utc)
|
||||
open_date = trade.open_date.astimezone(timezone.utc)
|
||||
dispo = round(self.wallets.get_available_stake_amount())
|
||||
hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
|
||||
days_since_first_buy = (current_time - trade.open_date_utc).days
|
||||
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
||||
count_of_buys = trade.nr_of_successful_entries
|
||||
current_time_utc = current_time.astimezone(timezone.utc)
|
||||
open_date = trade.open_date.astimezone(timezone.utc)
|
||||
days_since_open = (current_time_utc - open_date).days
|
||||
pair = trade.pair
|
||||
profit = trade.calc_profit(current_rate) #round(current_profit * trade.stake_amount, 1)
|
||||
last_lost = self.getLastLost(last_candle, pair)
|
||||
pct_first = 0
|
||||
|
||||
total_counts = sum(
|
||||
pair_data['count_of_buys'] for pair_data in self.pairs.values() if not self.getShortName(pair) == 'BTC')
|
||||
|
||||
if self.pairs[pair]['first_buy']:
|
||||
pct_first = self.getPctFirstBuy(pair, last_candle)
|
||||
|
||||
lim = 0.3
|
||||
if (len(dataframe) < 1):
|
||||
# self.printLog("skip dataframe")
|
||||
return None
|
||||
|
||||
# Dernier prix d'achat réel (pas le prix moyen)
|
||||
last_fill_price = self.pairs[trade.pair]['last_buy'] #trade.open_rate # remplacé juste après ↓
|
||||
|
||||
# if len(trade.orders) > 0:
|
||||
# # On cherche le dernier BUY exécuté
|
||||
# buy_orders = [o for o in trade.orders if o.is_buy and o.status == "closed"]
|
||||
# if buy_orders:
|
||||
# last_fill_price = buy_orders[-1].price
|
||||
|
||||
# baisse relative
|
||||
dca_threshold = 0.0025 * count_of_buys
|
||||
decline = (last_fill_price - current_rate) / last_fill_price
|
||||
increase = - decline
|
||||
|
||||
# if decline >= self.dca_threshold:
|
||||
# # Exemple : on achète 50% du montant du dernier trade
|
||||
# last_amount = buy_orders[-1].amount if buy_orders else 0
|
||||
# stake_amount = last_amount * current_rate * 0.5
|
||||
# return stake_amount
|
||||
|
||||
condition = last_candle['percent'] > 0 #and last_candle['sma24_deriv1'] > 0
|
||||
limit_buy = 40
|
||||
# or (last_candle['close'] <= last_candle['min180'] and hours > 3)
|
||||
if (decline >= dca_threshold) and condition:
|
||||
print('decline')
|
||||
try:
|
||||
if self.pairs[pair]['has_gain'] and profit > 0:
|
||||
self.pairs[pair]['force_sell'] = True
|
||||
self.pairs[pair]['previous_profit'] = profit
|
||||
return None
|
||||
|
||||
max_amount = self.config.get('stake_amount') * 2.5
|
||||
stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
|
||||
self.adjust_stake_amount(pair, last_candle))
|
||||
# print(f"profit={profit} previous={self.pairs[pair]['previous_profit']} count_of_buys={trade.nr_of_successful_entries}")
|
||||
if stake_amount > 0:
|
||||
self.pairs[pair]['previous_profit'] = profit
|
||||
trade_type = "Loss " + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
|
||||
self.pairs[trade.pair]['count_of_buys'] += 1
|
||||
self.pairs[pair]['total_amount'] += stake_amount
|
||||
# self.log_trade(
|
||||
# last_candle=last_candle,
|
||||
# date=current_time,
|
||||
# action="🟧 Loss -",
|
||||
# dispo=dispo,
|
||||
# pair=trade.pair,
|
||||
# rate=current_rate,
|
||||
# trade_type=trade_type,
|
||||
# profit=round(profit, 1),
|
||||
# buys=trade.nr_of_successful_entries + 1,
|
||||
# stake=round(stake_amount, 2)
|
||||
# )
|
||||
|
||||
self.pairs[trade.pair]['last_buy'] = current_rate
|
||||
self.pairs[trade.pair]['max_touch'] = last_candle['close']
|
||||
self.pairs[trade.pair]['last_candle'] = last_candle
|
||||
|
||||
# df = pd.DataFrame.from_dict(self.pairs, orient='index')
|
||||
# colonnes_a_exclure = ['last_candle', 'stop',
|
||||
# 'trade_info', 'last_date', 'expected_profit', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
|
||||
# df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
|
||||
# # df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
|
||||
#
|
||||
# self.printLog(df_filtered)
|
||||
|
||||
return stake_amount
|
||||
return None
|
||||
except Exception as exception:
|
||||
self.printLog(exception)
|
||||
return None
|
||||
|
||||
if current_profit > dca_threshold and (increase >= dca_threshold and self.wallets.get_available_stake_amount() > 0):
|
||||
print('increase')
|
||||
|
||||
try:
|
||||
self.pairs[pair]['previous_profit'] = profit
|
||||
stake_amount = max(20, min(self.wallets.get_available_stake_amount(), self.adjust_stake_amount(pair, last_candle)))
|
||||
if stake_amount > 0:
|
||||
self.pairs[pair]['has_gain'] += 1
|
||||
|
||||
trade_type = 'Gain +' + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
|
||||
self.pairs[trade.pair]['count_of_buys'] += 1
|
||||
self.pairs[pair]['total_amount'] += stake_amount
|
||||
# self.log_trade(
|
||||
# last_candle=last_candle,
|
||||
# date=current_time,
|
||||
# action="🟡 Gain +",
|
||||
# dispo=dispo,
|
||||
# pair=trade.pair,
|
||||
# rate=current_rate,
|
||||
# trade_type='Gain',
|
||||
# profit=round(profit, 1),
|
||||
# buys=trade.nr_of_successful_entries + 1,
|
||||
# stake=round(stake_amount, 2)
|
||||
# )
|
||||
self.pairs[trade.pair]['last_buy'] = current_rate
|
||||
self.pairs[trade.pair]['max_touch'] = last_candle['close']
|
||||
self.pairs[trade.pair]['last_candle'] = last_candle
|
||||
return stake_amount
|
||||
return None
|
||||
except Exception as exception:
|
||||
self.printLog(exception)
|
||||
return None
|
||||
|
||||
return None
|
||||
@@ -14,7 +14,10 @@ import pandas as pd # noqa
|
||||
from pandas import DataFrame
|
||||
from skopt.space import Categorical, Dimension, Integer, Real # noqa
|
||||
|
||||
from freqtrade.optimize.hyperopt_interface import IHyperOpt
|
||||
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
|
||||
IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
|
||||
|
||||
# from freqtrade.optimize.hyperopt_interface import IHyperOpt
|
||||
|
||||
# --------------------------------
|
||||
# Add your lib to import here
|
||||
@@ -52,7 +55,7 @@ GodGenes = ["open", "high", "low", "close", "volume", "volume_adi", "volume_obv"
|
||||
"others_dr", "others_dlr", "others_cr"]
|
||||
|
||||
|
||||
class GodStraHo(IHyperOpt):
|
||||
class GodStraHo(IStrategy):
|
||||
|
||||
@staticmethod
|
||||
def indicator_space() -> List[Dimension]:
|
||||
|
||||
53
GodStraJD.json
Normal file
53
GodStraJD.json
Normal file
@@ -0,0 +1,53 @@
|
||||
{
|
||||
"strategy_name": "GodStraJD",
|
||||
"params": {
|
||||
"roi": {
|
||||
"0": 0.598,
|
||||
"644": 0.166,
|
||||
"3269": 0.115,
|
||||
"7289": 0
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 80
|
||||
},
|
||||
"buy": {
|
||||
"buy_crossed_indicator0": "WMA-100",
|
||||
"buy_crossed_indicator1": "KAMA-110",
|
||||
"buy_crossed_indicator2": "CDLKICKING-55",
|
||||
"buy_indicator0": "MACD-0-50",
|
||||
"buy_indicator1": "SAREXT-15",
|
||||
"buy_indicator2": "DEMA-12",
|
||||
"buy_operator0": "<R",
|
||||
"buy_operator1": "C",
|
||||
"buy_operator2": "D",
|
||||
"buy_real_num0": 0.7,
|
||||
"buy_real_num1": 0.7,
|
||||
"buy_real_num2": 0.7
|
||||
},
|
||||
"sell": {
|
||||
"sell_crossed_indicator0": "CDLDARKCLOUDCOVER-15",
|
||||
"sell_crossed_indicator1": "CDLHANGINGMAN-6",
|
||||
"sell_crossed_indicator2": "CDLMATHOLD-55",
|
||||
"sell_indicator0": "MEDPRICE-15",
|
||||
"sell_indicator1": "T3-100",
|
||||
"sell_indicator2": "CDLDOJISTAR-12",
|
||||
"sell_operator0": "=R",
|
||||
"sell_operator1": "DT",
|
||||
"sell_operator2": "CA",
|
||||
"sell_real_num0": 0.7,
|
||||
"sell_real_num1": 0.7,
|
||||
"sell_real_num2": 0.6
|
||||
},
|
||||
"stoploss": {
|
||||
"stoploss": -0.122
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.058,
|
||||
"trailing_stop_positive_offset": 0.097,
|
||||
"trailing_only_offset_is_reached": true
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-04 23:29:46.366351+00:00"
|
||||
}
|
||||
@@ -470,7 +470,7 @@ class GodStraJD(IStrategy):
|
||||
# Stoploss:
|
||||
stoploss = -0.128
|
||||
# Buy hypers
|
||||
timeframe = '4h'
|
||||
timeframe = '1m'
|
||||
|
||||
# Trailing stoploss
|
||||
trailing_stop = True
|
||||
|
||||
@@ -474,7 +474,7 @@ class GodStraJD3_1(IStrategy):
|
||||
# Stoploss:
|
||||
stoploss = -1
|
||||
# Buy hypers
|
||||
timeframe = '4h'
|
||||
timeframe = '1m'
|
||||
|
||||
# Trailing stoploss
|
||||
trailing_stop = True
|
||||
|
||||
@@ -10,7 +10,7 @@ from freqtrade.persistence import Trade
|
||||
from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter
|
||||
|
||||
from numpy.lib import math
|
||||
from freqtrade.strategy.interface import IStrategy, SellCheckTuple
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
from pandas import DataFrame
|
||||
|
||||
# --------------------------------
|
||||
|
||||
@@ -668,8 +668,7 @@ class GodStraJD3_4(IStrategy):
|
||||
# # Use default stake amount.
|
||||
# return proposed_stake
|
||||
|
||||
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
|
||||
current_profit: float, **kwargs):
|
||||
def custom_exit(self, pair, trade, current_time, current_rate, current_profit, **kwargs):
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
previous_last_candle = dataframe.iloc[-2].squeeze()
|
||||
@@ -971,8 +970,6 @@ class GodStraJD3_4(IStrategy):
|
||||
if conditions:
|
||||
dataframe.loc[
|
||||
(reduce(lambda x, y: x & y, conditions)
|
||||
# & (dataframe['percent_4h'] > 0)
|
||||
& (dataframe['percent3_4h'] <= 0)
|
||||
),
|
||||
'buy']=1
|
||||
# print(len(dataframe.keys()))
|
||||
|
||||
@@ -10,7 +10,7 @@ from freqtrade.persistence import Trade
|
||||
from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter, IntParameter
|
||||
|
||||
from numpy.lib import math
|
||||
from freqtrade.strategy.interface import IStrategy, SellCheckTuple
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
from pandas import DataFrame
|
||||
|
||||
# --------------------------------
|
||||
|
||||
@@ -10,7 +10,7 @@ from freqtrade.persistence import Trade
|
||||
from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter, IntParameter
|
||||
|
||||
from numpy.lib import math
|
||||
from freqtrade.strategy.interface import IStrategy, SellCheckTuple
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
from pandas import DataFrame
|
||||
|
||||
# --------------------------------
|
||||
|
||||
@@ -27,7 +27,7 @@ import numpy as np
|
||||
from random import shuffle
|
||||
|
||||
# TODO: this gene is removed 'MAVP' cuz or error on periods
|
||||
import user_data.strategies.custom_indicators as csa
|
||||
# import user_data.strategies.custom_indicators as csa
|
||||
|
||||
all_god_genes = {
|
||||
'Overlap Studies': {
|
||||
|
||||
@@ -86,7 +86,7 @@ class HammerReversalStrategy(IStrategy):
|
||||
}
|
||||
|
||||
stoploss = -1
|
||||
timeframe = '1h'
|
||||
timeframe = '1m'
|
||||
position_adjustment_enable = True
|
||||
columns_logged = False
|
||||
max_entry_position_adjustment = 20
|
||||
|
||||
@@ -220,10 +220,10 @@ class Ishimoku_6(IStrategy):
|
||||
def bot_loop_start(self, **kwargs) -> None:
|
||||
inf_tf = '5m'
|
||||
pairs = self.dp.current_whitelist()
|
||||
print("Calcul des pairs informatives")
|
||||
# print("Calcul des pairs informatives")
|
||||
for pairname in pairs:
|
||||
self.stop_buying[pairname] = True
|
||||
print("Fin Calcul des pairs informatives")
|
||||
# print("Fin Calcul des pairs informatives")
|
||||
|
||||
# def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
|
||||
# proposed_stake: float, min_stake: float, max_stake: float,
|
||||
|
||||
265
Simple.py
Normal file
265
Simple.py
Normal file
@@ -0,0 +1,265 @@
|
||||
# Zeus Strategy: First Generation of GodStra Strategy with maximum
|
||||
# AVG/MID profit in USDT
|
||||
# Author: @Mablue (Masoud Azizi)
|
||||
# github: https://github.com/mablue/
|
||||
# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
|
||||
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus
|
||||
# --- Do not remove these libs ---
|
||||
from datetime import timedelta, datetime
|
||||
from freqtrade.persistence import Trade
|
||||
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
|
||||
IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from pandas import DataFrame
|
||||
from typing import Optional, Union, Tuple
|
||||
from typing import List
|
||||
|
||||
import logging
|
||||
import configparser
|
||||
from technical import pivots_points
|
||||
# --------------------------------
|
||||
|
||||
# Add your lib to import here test git
|
||||
import ta
|
||||
import talib.abstract as talib
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
import requests
|
||||
from datetime import timezone, timedelta
|
||||
from scipy.signal import savgol_filter
|
||||
from ta.trend import SMAIndicator, EMAIndicator, MACD, ADXIndicator
|
||||
from collections import Counter
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
from tabulate import tabulate
|
||||
|
||||
# Couleurs ANSI de base
|
||||
RED = "\033[31m"
|
||||
GREEN = "\033[32m"
|
||||
YELLOW = "\033[33m"
|
||||
BLUE = "\033[34m"
|
||||
MAGENTA = "\033[35m"
|
||||
CYAN = "\033[36m"
|
||||
RESET = "\033[0m"
|
||||
|
||||
|
||||
def pprint_df(dframe):
|
||||
print(tabulate(dframe, headers='keys', tablefmt='psql', showindex=False))
|
||||
|
||||
|
||||
def normalize(df):
|
||||
df = (df - df.min()) / (df.max() - df.min())
|
||||
return df
|
||||
|
||||
"""
|
||||
SMA z-score derivative strategy with trailing exit (large).
|
||||
- timeframe: 1h
|
||||
- sma5, relative derivatives, z-score normalization (rolling z over z_window)
|
||||
- smoothing: ewm(span=5) on z-scores
|
||||
- entry: z_d1_smooth > entry_z1 AND z_d2_smooth > entry_z2
|
||||
- exit: inversion (z_d1_smooth < 0) AND retrace from highest since entry >= trailing_stop
|
||||
"""
|
||||
class Simple(IStrategy):
|
||||
levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
|
||||
startup_candle_count = 12 * 24 * 2
|
||||
|
||||
# ROI table:
|
||||
minimal_roi = {
|
||||
"0": 10
|
||||
}
|
||||
stakes = 40
|
||||
|
||||
# Stoploss:
|
||||
stoploss = -1 # 0.256
|
||||
# Custom stoploss
|
||||
use_custom_stoploss = True
|
||||
|
||||
# Buy hypers
|
||||
timeframe = '1h'
|
||||
|
||||
max_open_trades = 5
|
||||
max_amount = 40
|
||||
|
||||
# DCA config
|
||||
position_adjustment_enable = True
|
||||
|
||||
# Parameters (tweakable)
|
||||
z_window = 50 # window for rolling mean/std to compute zscore
|
||||
entry_z1 = 0.1 # threshold on z-score of first derivative
|
||||
entry_z2 = 0.1 # threshold on z-score of second derivative
|
||||
min_volume = 0.0 # minimal volume to accept an entry
|
||||
min_relative_d1 = 1e-6 # clip tiny d1 relative values to reduce noise
|
||||
|
||||
# Trailing parameters for "large" trailing requested
|
||||
trailing_stop_pct = 0.05 # 5% retracement from highest since entry
|
||||
|
||||
# Smoothing for z-scores
|
||||
ewm_span = 5
|
||||
|
||||
# Plot config: price + sma5 + markers + slope/accel subplots
|
||||
plot_config = {
|
||||
"main_plot": {
|
||||
"close": {"color": "blue"},
|
||||
"sma5": {"color": "orange"},
|
||||
},
|
||||
"subplots": {
|
||||
"slope_and_accel": {
|
||||
"z_d1": {"color": "green"},
|
||||
"z_d2": {"color": "red"},
|
||||
}
|
||||
},
|
||||
# Markers (Freqtrade charting supports markers via these keys)
|
||||
"markers": [
|
||||
# buy marker: '^' green when enter_long==1
|
||||
{"type": "buy", "column": "enter_long", "marker": "^", "color": "green", "markersize": 10},
|
||||
# sell marker: 'v' red when exit_long==1
|
||||
{"type": "sell", "column": "exit_long", "marker": "v", "color": "red", "markersize": 10},
|
||||
],
|
||||
}
|
||||
|
||||
def informative_pairs(self):
|
||||
return []
|
||||
|
||||
def _zscore(self, series: pd.Series, window: int) -> pd.Series:
|
||||
mean = series.rolling(window=window, min_periods=3).mean()
|
||||
std = series.rolling(window=window, min_periods=3).std(ddof=0)
|
||||
z = (series - mean) / std
|
||||
z = z.replace([np.inf, -np.inf], np.nan)
|
||||
return z
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
df = dataframe
|
||||
|
||||
# SMA(5)
|
||||
df['sma5'] = df['close'].rolling(5, min_periods=1).mean()
|
||||
|
||||
# Absolute derivatives
|
||||
df['sma5_d1'] = df['sma5'].diff().rolling(5).mean()
|
||||
df['sma5_d2'] = df['sma5_d1'].diff().rolling(5).mean()
|
||||
|
||||
# Relative derivatives (percentage-like)
|
||||
eps = 1e-9
|
||||
df['sma5_d1_rel'] = df['sma5_d1'] / (df['sma5'].shift(1).replace(0, np.nan) + eps)
|
||||
df['sma5_d2_rel'] = df['sma5_d2'] / (df['sma5'].shift(2).replace(0, np.nan) + eps)
|
||||
|
||||
# Clip micro-noise
|
||||
df.loc[df['sma5_d1_rel'].abs() < self.min_relative_d1, 'sma5_d1_rel'] = 0.0
|
||||
df.loc[df['sma5_d2_rel'].abs() < self.min_relative_d1, 'sma5_d2_rel'] = 0.0
|
||||
|
||||
# Z-scores on relative derivatives
|
||||
df['z_d1'] = self._zscore(df['sma5_d1_rel'], self.z_window)
|
||||
df['z_d2'] = self._zscore(df['sma5_d2_rel'], self.z_window)
|
||||
|
||||
# Smoothing z-scores with EWM to reduce jitter
|
||||
df['z_d1_smooth'] = df['z_d1'].ewm(span=self.ewm_span, adjust=False).mean()
|
||||
df['z_d2_smooth'] = df['z_d2'].ewm(span=self.ewm_span, adjust=False).mean()
|
||||
|
||||
# Prepare marker columns (for plots). They will be filled in populate_entry_trend/populate_exit_trend
|
||||
df['enter_long'] = 0
|
||||
df['exit_long'] = 0
|
||||
|
||||
return df
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
df = dataframe.copy()
|
||||
|
||||
# Use last closed candle for signals -> shift(1)
|
||||
cond_entry = (
|
||||
(df['z_d1'] > self.entry_z1) &
|
||||
(df['z_d2'] > self.entry_z2) &
|
||||
(df['volume'].shift(1) > self.min_volume)
|
||||
)
|
||||
|
||||
df.loc[cond_entry, 'enter_long'] = 1
|
||||
# Ensure others are explicitly zero (for clean plotting)
|
||||
df.loc[~cond_entry, 'enter_long'] = 0
|
||||
|
||||
return df
|
||||
|
||||
def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs) -> \
|
||||
Optional[str]:
|
||||
"""
|
||||
Exit policy (mode C - trailing large):
|
||||
- Must detect inversion: z_d1_smooth < 0 on last closed candle
|
||||
- Compute highest close since trade entry (inclusive)
|
||||
- If price has retraced >= trailing_stop_pct from that highest point and we're in inversion -> exit
|
||||
"""
|
||||
|
||||
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last = df.iloc[-1].squeeze()
|
||||
|
||||
z1 = last.get('z_d1_smooth', None)
|
||||
if z1 is None:
|
||||
return None
|
||||
|
||||
# Only consider exits when inversion detected (z1 < 0)
|
||||
inversion = (z1 < 0)
|
||||
|
||||
if not inversion:
|
||||
return None
|
||||
|
||||
# If we don't have profit info, be conservative
|
||||
if current_profit is None:
|
||||
return None
|
||||
|
||||
# Determine highest close since entry
|
||||
highest_since_entry = None
|
||||
try:
|
||||
# trade.open_date_utc is available: find rows after that timestamp
|
||||
# df.index is expected to be pd.DatetimeIndex in UTC or local; convert safely
|
||||
if hasattr(trade, 'open_date_utc') and trade.open_date_utc:
|
||||
# pandas comparison: ensure same tz awareness
|
||||
entry_time = pd.to_datetime(trade.open_date_utc)
|
||||
# select rows with index >= entry_time
|
||||
mask = df.index >= entry_time
|
||||
if mask.any():
|
||||
highest_since_entry = df.loc[mask, 'close'].max()
|
||||
# fallback: use trade.open_rate or the max over full df
|
||||
except Exception as e:
|
||||
logger.debug(f"Couldn't compute highest_since_entry from open_date_utc: {e}")
|
||||
|
||||
if highest_since_entry is None:
|
||||
# fallback: use the maximum close in the entire provided dataframe slice
|
||||
highest_since_entry = df['close'].max() if not df['close'].empty else current_rate
|
||||
|
||||
# Calculate retracement ratio from the highest
|
||||
if highest_since_entry and highest_since_entry > 0:
|
||||
retrace = 1.0 - (current_rate / highest_since_entry)
|
||||
else:
|
||||
retrace = 0.0
|
||||
|
||||
# Exit if:
|
||||
# - currently in profit AND
|
||||
# - retracement >= trailing_stop_pct (i.e. price has fallen enough from top since entry) AND
|
||||
# - inversion detected (z1 < 0)
|
||||
if (current_profit > 0) and (retrace >= self.trailing_stop_pct):
|
||||
# Mark the dataframe for plotting (if possible)
|
||||
# Note: freqtrade expects strategies to set exit flags in populate_exit_trend,
|
||||
# but we set exit via custom_exit return reason; plotting will read exit_long if populated.
|
||||
return "zscore"
|
||||
|
||||
# Otherwise, do not exit yet
|
||||
return None
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
For plotting only: mark exit points where our logic would trigger.
|
||||
This is approximate: we mark an exit when z_d1_smooth < 0 and the price has retraced >= trailing_stop_pct
|
||||
based on the available dataframe window (best-effort).
|
||||
"""
|
||||
df = dataframe
|
||||
# df['exit_long'] = 0
|
||||
#
|
||||
# # compute highest close since each possible entry (best-effort: use rolling max up to current index)
|
||||
# rolling_max = df['close'].cummax()
|
||||
#
|
||||
# # retracement relative to rolling max
|
||||
# retrace = 1.0 - (df['close'] / rolling_max.replace(0, np.nan))
|
||||
#
|
||||
# # mark exits where inversion and retrace >= threshold
|
||||
# cond_exit = (df['z_d1_smooth'] < 0) & (retrace >= self.trailing_stop_pct)
|
||||
# # shift by 0: we mark the candle where the exit condition appears
|
||||
# df.loc[cond_exit, 'exit_long'] = 1
|
||||
|
||||
return df
|
||||
633
Simple_01.py
Normal file
633
Simple_01.py
Normal file
@@ -0,0 +1,633 @@
|
||||
# sma_zscore_trailing_dca.py
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
from freqtrade.persistence import Trade
|
||||
from pandas import DataFrame
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Optional
|
||||
import logging
|
||||
from datetime import timedelta, datetime
|
||||
import talib.abstract as talib
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class Simple_01(IStrategy):
|
||||
"""
|
||||
SMA z-score derivative strategy with trailing exit and intelligent DCA (large pullback).
|
||||
- timeframe: 1h
|
||||
- sma5, relative derivatives, z-score normalization (rolling z over z_window)
|
||||
- smoothing: ewm(span=5) on z-scores
|
||||
- entry: z_d1_smooth > entry_z1 AND z_d2_smooth > entry_z2
|
||||
- exit: inversion (z_d1_smooth < 0) AND retrace from highest since entry >= trailing_stop_pct
|
||||
- adjust_trade_position: add to position on controlled pullback (large = 4-6%)
|
||||
"""
|
||||
|
||||
timeframe = "1h"
|
||||
startup_candle_count = 24
|
||||
# Risk mgmt (we handle exit in custom_exit)
|
||||
minimal_roi = {"0": 0.99}
|
||||
stoploss = -0.99
|
||||
use_custom_exit = True
|
||||
position_adjustment_enable = True
|
||||
|
||||
columns_logged = False
|
||||
|
||||
# Parameters
|
||||
z_window = 10 # window for rolling mean/std to compute zscore
|
||||
entry_z1 = 0.1 # threshold on z-score of first derivative
|
||||
entry_z2 = 0 # threshold on z-score of second derivative
|
||||
min_volume = 0.0 # minimal volume to accept an entry
|
||||
min_relative_d1 = 1e-6 # clip tiny d1 relative values to reduce noise
|
||||
|
||||
# Trailing parameters for "large" trailing (exit)
|
||||
trailing_stop_pct = 0.01 # 5% retracement from highest since entry
|
||||
|
||||
# Smoothing for z-scores
|
||||
ewm_span = 5
|
||||
|
||||
# DCA intelligent (adjust_trade_position) parameters for "large" pullback
|
||||
dca_enabled = True
|
||||
# Pullback bounds (large): allow adding when retrace is between 4% and 6%
|
||||
dca_pullback_min = 0.01
|
||||
dca_pullback_max = 0.02
|
||||
# Maximum number of adds per trade (to avoid infinite pyramiding)
|
||||
dca_max_adds = 8
|
||||
# Percentage of base position to add on each reinforcement (50% of original size by default)
|
||||
dca_add_ratio = 0.5
|
||||
# Require momentum still positive to add
|
||||
dca_require_z1_positive = True
|
||||
dca_require_z2_positive = True
|
||||
# Do not add if current_profit < min_profit_to_add (avoid averaging down when deep in loss)
|
||||
min_profit_to_add = -0.02 # allow small loss but not big drawdown
|
||||
|
||||
pairs = {
|
||||
pair: {
|
||||
"first_buy": 0,
|
||||
"last_buy": 0.0,
|
||||
"first_amount": 0.0,
|
||||
"last_min": 999999999999999.5,
|
||||
"last_max": 0,
|
||||
"trade_info": {},
|
||||
"max_touch": 0.0,
|
||||
"last_sell": 0.0,
|
||||
'count_of_buys': 0,
|
||||
'current_profit': 0,
|
||||
'expected_profit': 0,
|
||||
"last_candle": {},
|
||||
"last_trade": None,
|
||||
"last_count_of_buys": 0,
|
||||
'base_stake_amount': 0,
|
||||
'stop_buy': False,
|
||||
'last_date': 0,
|
||||
'stop': False,
|
||||
'max_profit': 0,
|
||||
'last_palier_index': -1,
|
||||
'total_amount': 0,
|
||||
'has_gain': 0,
|
||||
'force_sell': False,
|
||||
'force_buy': False
|
||||
}
|
||||
for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC",
|
||||
"BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"]
|
||||
}
|
||||
|
||||
# Plot config
|
||||
plot_config = {
|
||||
"main_plot": {
|
||||
"close": {"color": "blue"},
|
||||
"sma5": {"color": "orange"},
|
||||
},
|
||||
"subplots": {
|
||||
"slope_and_accel": {
|
||||
"z_d1_smooth": {"color": "green"},
|
||||
"z_d2_smooth": {"color": "red"},
|
||||
},
|
||||
"sma_deriv": {
|
||||
"sma5_deriv1_rel": {"color": "green"},
|
||||
"sma5_deriv2_rel": {"color": "red"},
|
||||
},
|
||||
"rsi": {
|
||||
"rsi": {"color": "blue"}
|
||||
}
|
||||
|
||||
},
|
||||
"markers": [
|
||||
{"type": "buy", "column": "enter_long", "marker": "^", "color": "green", "markersize": 10},
|
||||
{"type": "sell", "column": "exit_long", "marker": "v", "color": "red", "markersize": 10},
|
||||
],
|
||||
}
|
||||
|
||||
def informative_pairs(self):
|
||||
return []
|
||||
|
||||
def _zscore(self, series: pd.Series, window: int) -> pd.Series:
|
||||
mean = series.rolling(window=window, min_periods=3).mean()
|
||||
std = series.rolling(window=window, min_periods=3).std(ddof=0)
|
||||
z = (series - mean) / std
|
||||
z = z.replace([np.inf, -np.inf], np.nan)
|
||||
return z
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
df = dataframe
|
||||
|
||||
# SMA(5)
|
||||
df['sma5'] = df['close'].rolling(5, min_periods=1).mean()
|
||||
|
||||
# Absolute derivatives
|
||||
df['sma5_deriv1'] = df['sma5'].diff()
|
||||
df['sma5_deriv2'] = df['sma5_deriv1'].diff()
|
||||
|
||||
# Relative derivatives (percentage-like)
|
||||
eps = 1e-9
|
||||
df['sma5_deriv1_rel'] = df['sma5_deriv1'] / (df['sma5'].shift(1).replace(0, np.nan) + eps)
|
||||
df['sma5_deriv2_rel'] = df['sma5_deriv2'] / (df['sma5'].shift(2).replace(0, np.nan) + eps)
|
||||
|
||||
# Clip micro-noise
|
||||
df.loc[df['sma5_deriv1_rel'].abs() < self.min_relative_d1, 'sma5_deriv1_rel'] = 0.0
|
||||
df.loc[df['sma5_deriv2_rel'].abs() < self.min_relative_d1, 'sma5_deriv2_rel'] = 0.0
|
||||
|
||||
# Z-scores on relative derivatives
|
||||
df['z_d1'] = self._zscore(df['sma5_deriv1_rel'], self.z_window)
|
||||
df['z_d2'] = self._zscore(df['sma5_deriv2_rel'], self.z_window)
|
||||
|
||||
# Smoothing z-scores with EWM to reduce jitter
|
||||
df['z_d1_smooth'] = df['z_d1'].ewm(span=self.ewm_span, adjust=False).mean()
|
||||
df['z_d2_smooth'] = df['z_d2'].ewm(span=self.ewm_span, adjust=False).mean()
|
||||
|
||||
# Prepare marker columns (for plots)
|
||||
df['enter_long'] = 0
|
||||
df['exit_long'] = 0
|
||||
df['rsi'] = talib.RSI(df['close'], timeperiod=14)
|
||||
df['max_rsi_12'] = talib.MAX(df['rsi'], timeperiod=12)
|
||||
df['min_rsi_12'] = talib.MIN(df['rsi'], timeperiod=12)
|
||||
# self.calculeDerivees(df, 'rsi', horizon=12)
|
||||
|
||||
return df
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
df = dataframe
|
||||
|
||||
# Use last closed candle for signals -> shift(1)
|
||||
cond_entry = (
|
||||
(df['z_d1_smooth'] > self.entry_z1) &
|
||||
(df['z_d2_smooth'] > self.entry_z2) &
|
||||
(df['max_rsi_12'] < 70) &
|
||||
(df['volume'] > self.min_volume)
|
||||
)
|
||||
|
||||
df.loc[cond_entry, 'enter_long'] = 1
|
||||
df.loc[~cond_entry, 'enter_long'] = 0
|
||||
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
df = dataframe
|
||||
# df['exit_long'] = 0
|
||||
#
|
||||
# # compute rolling max (best-effort for plotting)
|
||||
# rolling_max = df['close'].cummax()
|
||||
# retrace = 1.0 - (df['close'] / rolling_max.replace(0, np.nan))
|
||||
#
|
||||
# cond_exit = (df['z_d1_smooth'] < 0) & (retrace >= self.trailing_stop_pct)
|
||||
# df.loc[cond_exit, 'exit_long'] = 1
|
||||
|
||||
return df
|
||||
|
||||
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str,
|
||||
exit_reason: str, current_time, **kwargs, ) -> bool:
|
||||
# allow_to_sell = (minutes > 30)
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
|
||||
force = self.pairs[pair]['force_sell']
|
||||
allow_to_sell = True #(last_candle['percent'] < 0) #or force
|
||||
|
||||
minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
|
||||
|
||||
if allow_to_sell:
|
||||
self.trades = list()
|
||||
self.pairs[pair]['last_count_of_buys'] = trade.nr_of_successful_entries # self.pairs[pair]['count_of_buys']
|
||||
self.pairs[pair]['last_sell'] = rate
|
||||
self.pairs[pair]['last_trade'] = trade
|
||||
self.pairs[pair]['last_candle'] = last_candle
|
||||
self.trades = list()
|
||||
dispo = round(self.wallets.get_available_stake_amount())
|
||||
print(f"Sell {pair} {current_time} {exit_reason} dispo={dispo} amount={amount} rate={rate} open_rate={trade.open_rate}")
|
||||
self.log_trade(
|
||||
last_candle=last_candle,
|
||||
date=current_time,
|
||||
action="🟥Sell " + str(minutes),
|
||||
pair=pair,
|
||||
trade_type=exit_reason,
|
||||
rate=last_candle['close'],
|
||||
dispo=dispo,
|
||||
profit=round(trade.calc_profit(rate, amount), 2)
|
||||
)
|
||||
self.pairs[pair]['max_profit'] = 0
|
||||
self.pairs[pair]['force_sell'] = False
|
||||
self.pairs[pair]['has_gain'] = 0
|
||||
self.pairs[pair]['current_profit'] = 0
|
||||
self.pairs[pair]['total_amount'] = 0
|
||||
self.pairs[pair]['count_of_buys'] = 0
|
||||
self.pairs[pair]['max_touch'] = 0
|
||||
self.pairs[pair]['last_buy'] = 0
|
||||
self.pairs[pair]['last_date'] = current_time
|
||||
self.pairs[pair]['last_palier_index'] = -1
|
||||
self.pairs[pair]['last_trade'] = trade
|
||||
self.pairs[pair]['current_trade'] = None
|
||||
|
||||
return (allow_to_sell) | (exit_reason == 'force_exit')
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
|
||||
current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
|
||||
|
||||
minutes = 0
|
||||
if self.pairs[pair]['last_date'] != 0:
|
||||
minutes = round(int((current_time - self.pairs[pair]['last_date']).total_seconds() / 60))
|
||||
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
last_candle_2 = dataframe.iloc[-2].squeeze()
|
||||
last_candle_3 = dataframe.iloc[-3].squeeze()
|
||||
# val = self.getProbaHausse144(last_candle)
|
||||
|
||||
# allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
|
||||
allow_to_buy = not self.pairs[pair]['stop'] # and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--') # (rate <= float(limit)) | (entry_tag == 'force_entry')
|
||||
|
||||
# force = self.pairs[pair]['force_buy']
|
||||
# if self.pairs[pair]['force_buy']:
|
||||
# self.pairs[pair]['force_buy'] = False
|
||||
# allow_to_buy = True
|
||||
# else:
|
||||
# if not self.should_enter_trade(pair, last_candle, current_time):
|
||||
# allow_to_buy = False
|
||||
|
||||
if allow_to_buy:
|
||||
self.trades = list()
|
||||
self.pairs[pair]['first_buy'] = rate
|
||||
self.pairs[pair]['last_buy'] = rate
|
||||
self.pairs[pair]['max_touch'] = last_candle['close']
|
||||
self.pairs[pair]['last_candle'] = last_candle
|
||||
self.pairs[pair]['count_of_buys'] = 1
|
||||
self.pairs[pair]['current_profit'] = 0
|
||||
self.pairs[pair]['last_palier_index'] = -1
|
||||
self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
|
||||
self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
|
||||
|
||||
dispo = round(self.wallets.get_available_stake_amount())
|
||||
self.printLineLog()
|
||||
|
||||
stake_amount = self.adjust_stake_amount(pair, last_candle)
|
||||
|
||||
self.pairs[pair]['total_amount'] = stake_amount
|
||||
|
||||
self.log_trade(
|
||||
last_candle=last_candle,
|
||||
date=current_time,
|
||||
action=("🟩Buy" if allow_to_buy else "Canceled") + " " + str(minutes),
|
||||
pair=pair,
|
||||
rate=rate,
|
||||
dispo=dispo,
|
||||
profit=0,
|
||||
trade_type=entry_tag,
|
||||
buys=1,
|
||||
stake=round(stake_amount, 2)
|
||||
)
|
||||
|
||||
return allow_to_buy
|
||||
|
||||
def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs) -> Optional[str]:
|
||||
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = df.iloc[-1].squeeze()
|
||||
|
||||
self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
|
||||
self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
|
||||
self.pairs[pair]['current_trade'] = trade
|
||||
count_of_buys = trade.nr_of_successful_entries
|
||||
|
||||
profit = round(current_profit * trade.stake_amount, 1)
|
||||
self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], profit)
|
||||
max_profit = self.pairs[pair]['max_profit']
|
||||
baisse = 0
|
||||
if profit > 0:
|
||||
baisse = 100 * abs(max_profit - profit) / max_profit
|
||||
mx = max_profit / 5
|
||||
self.pairs[pair]['count_of_buys'] = count_of_buys
|
||||
self.pairs[pair]['current_profit'] = profit
|
||||
|
||||
dispo = round(self.wallets.get_available_stake_amount())
|
||||
hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
|
||||
days_since_first_buy = (current_time - trade.open_date_utc).days
|
||||
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
||||
|
||||
if hours % 4 == 0:
|
||||
self.log_trade(
|
||||
last_candle=last_candle,
|
||||
date=current_time,
|
||||
action="🔴 CURRENT" if self.pairs[pair]['stop'] else "🟢 CURRENT",
|
||||
dispo=dispo,
|
||||
pair=pair,
|
||||
rate=last_candle['close'],
|
||||
trade_type='',
|
||||
profit=profit,
|
||||
buys='',
|
||||
stake=0
|
||||
)
|
||||
|
||||
z1 = last_candle.get('z_d1_smooth', None)
|
||||
if z1 is None:
|
||||
return None
|
||||
|
||||
if z1 >= 0:
|
||||
return None
|
||||
|
||||
if current_profit is None:
|
||||
return None
|
||||
|
||||
# highest close since entry
|
||||
highest_since_entry = self.pairs[trade.pair]['max_touch']
|
||||
# try:
|
||||
# if hasattr(trade, 'open_date_utc') and trade.open_date_utc:
|
||||
# entry_time = pd.to_datetime(trade.open_date_utc)
|
||||
# mask = df.index >= entry_time
|
||||
# if mask.any():
|
||||
# highest_since_entry = df.loc[mask, 'close'].max()
|
||||
# except Exception as e:
|
||||
# logger.debug(f"Couldn't compute highest_since_entry: {e}")
|
||||
#
|
||||
# if highest_since_entry is None:
|
||||
# highest_since_entry = df['close'].max() if not df['close'].empty else current_rate
|
||||
|
||||
if highest_since_entry and highest_since_entry > 0:
|
||||
retrace = 1.0 - (current_rate / highest_since_entry)
|
||||
else:
|
||||
retrace = 0.0
|
||||
z1 = last_candle.get('z_d1_smooth', None)
|
||||
z2 = last_candle.get('z_d2_smooth', None)
|
||||
|
||||
if (current_profit > 0) and (current_profit >= self.trailing_stop_pct) and last_candle['sma5_deriv1'] < -0.002:
|
||||
return str(count_of_buys) + '_' + "zscore"
|
||||
|
||||
self.pairs[pair]['max_touch'] = max(last_candle['close'], self.pairs[pair]['max_touch'])
|
||||
|
||||
return None
|
||||
|
||||
def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
|
||||
# Calculer le minimum des 14 derniers jours
|
||||
return self.config.get('stake_amount')
|
||||
|
||||
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, min_stake: float,
|
||||
max_stake: float, **kwargs):
|
||||
"""
|
||||
DCA intelligent (mode C - 'large'):
|
||||
- Only add if:
|
||||
* DCA is enabled
|
||||
* number of adds done so far < dca_max_adds
|
||||
* retracement since highest_since_entry is between dca_pullback_min and dca_pullback_max
|
||||
* momentum still positive (z_d1_smooth, z_d2_smooth) if required
|
||||
* current_profit >= min_profit_to_add (avoid averaging down into large loss)
|
||||
- Returns a dict describing the desired order for Freqtrade to place (common format).
|
||||
Example returned dict: {'type': 'market', 'amount': 0.01}
|
||||
"""
|
||||
if not self.dca_enabled:
|
||||
return None
|
||||
pair = trade.pair
|
||||
|
||||
# Basic guards
|
||||
if current_profit is None:
|
||||
current_profit = 0.0
|
||||
|
||||
# Do not add if we're already deeply in loss
|
||||
if current_profit < self.min_profit_to_add:
|
||||
return None
|
||||
|
||||
df, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
||||
last_candle = df.iloc[-1].squeeze()
|
||||
|
||||
# Compute highest close since entry
|
||||
highest_since_entry = self.pairs[trade.pair]['last_buy']
|
||||
|
||||
last = df.iloc[-1]
|
||||
z1 = last.get('z_d1_smooth', None)
|
||||
z2 = last.get('z_d2_smooth', None)
|
||||
|
||||
# Count how many adds have been done for this trade.
|
||||
# Trade.extra might contain meta; otherwise try trade.tags or use trade.open_rate/amount heuristics.
|
||||
adds_done = trade.nr_of_successful_entries
|
||||
# Calculate retracement since the highest
|
||||
retrace = 0.0
|
||||
if highest_since_entry and highest_since_entry > 0:
|
||||
retrace = (last['close'] - highest_since_entry) / highest_since_entry
|
||||
|
||||
# logger.info(f"{pair} {current_rate} {current_time} {highest_since_entry} add: retrace={retrace:.4f}, adds_done={adds_done} z1={z1} z2={z2}")
|
||||
|
||||
# Enforce momentum requirements if requested
|
||||
if self.dca_require_z1_positive and (z1 is None or z1 <= 0):
|
||||
return None
|
||||
if self.dca_require_z2_positive and (z2 is None or z2 <= 0):
|
||||
return None
|
||||
|
||||
#
|
||||
# try:
|
||||
# meta = getattr(trade, 'meta', None) or {}
|
||||
# adds_done = int(meta.get('adds_done', 0))
|
||||
# except Exception:
|
||||
# adds_done = 0
|
||||
|
||||
if adds_done >= self.dca_max_adds:
|
||||
return None
|
||||
|
||||
# try:
|
||||
# if hasattr(trade, 'open_date_utc') and trade.open_date_utc:
|
||||
# entry_time = pd.to_datetime(trade.open_date_utc)
|
||||
# mask = df.index >= entry_time
|
||||
# if mask.any():
|
||||
# highest_since_entry = df.loc[mask, 'close'].max()
|
||||
# except Exception as e:
|
||||
# logger.debug(f"adjust_trade_position: couldn't compute highest_since_entry: {e}")
|
||||
#
|
||||
# if highest_since_entry is None:
|
||||
# highest_since_entry = df['close'].max() if not df['close'].empty else current_rate
|
||||
|
||||
# Check if retrace is inside the allowed DCA window (large)
|
||||
if ((retrace >= self.dca_pullback_min) and (retrace <= self.dca_pullback_max)):
|
||||
# Determine amount to add: a fraction of the original trade amount
|
||||
# Try to get trade.amount (base asset amount). If not available, fall back to stake percentage
|
||||
add_amount = None
|
||||
try:
|
||||
base_amount = self.config.get('stake_amount')
|
||||
if base_amount:
|
||||
add_amount = base_amount * self.dca_add_ratio
|
||||
else:
|
||||
# Fallback: attempt to compute amount from trade.open_rate and desired quote stake
|
||||
# We'll propose to use a fraction of current rate worth (this is best-effort)
|
||||
add_amount = None
|
||||
except Exception:
|
||||
add_amount = None
|
||||
|
||||
# If we couldn't compute an absolute amount, propose a relative size via a suggested stake (user must map)
|
||||
if add_amount is None:
|
||||
# Return a suggested instruction; adapt according to your freqtrade version.
|
||||
suggested = {
|
||||
'type': 'market',
|
||||
'amount': None, # caller should compute actual amount from stake management
|
||||
'note': f'suggest_add_ratio={self.dca_add_ratio}'
|
||||
}
|
||||
# logger.info(f"{pair} {current_rate} DCA suggestion (no absolute amount): retrace={retrace:.4f}, adds_done={adds_done}")
|
||||
return None
|
||||
|
||||
dispo = round(self.wallets.get_available_stake_amount())
|
||||
trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
|
||||
self.pairs[trade.pair]['count_of_buys'] += 1
|
||||
self.pairs[pair]['total_amount'] += add_amount
|
||||
self.log_trade(
|
||||
last_candle=last_candle,
|
||||
date=current_time,
|
||||
action="🟧 Loss -",
|
||||
dispo=dispo,
|
||||
pair=trade.pair,
|
||||
rate=current_rate,
|
||||
trade_type=trade_type,
|
||||
profit=round(current_profit * trade.stake_amount, 1),
|
||||
buys=trade.nr_of_successful_entries + 1,
|
||||
stake=round(add_amount, 2)
|
||||
)
|
||||
|
||||
self.pairs[trade.pair]['last_buy'] = current_rate
|
||||
self.pairs[trade.pair]['max_touch'] = last_candle['close']
|
||||
self.pairs[trade.pair]['last_candle'] = last_candle
|
||||
|
||||
# All checks passed -> create market order instruction
|
||||
# logger.info(f"{pair} {current_rate} {current_time} {highest_since_entry} add: retrace={retrace:.4f}, adds_done={adds_done}, add_amount={add_amount:.8f}")
|
||||
return add_amount
|
||||
|
||||
# Not in allowed retrace window -> no action
|
||||
return None
|
||||
|
||||
def getPctFirstBuy(self, pair, last_candle):
|
||||
return round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
|
||||
|
||||
def getPctLastBuy(self, pair, last_candle):
|
||||
return round((last_candle['close'] - self.pairs[pair]['last_buy']) / self.pairs[pair]['last_buy'], 4)
|
||||
|
||||
def getPct60D(self, pair, last_candle):
|
||||
return round((last_candle['max60'] - last_candle['min60']) / last_candle['max60'], 4)
|
||||
|
||||
def getPctClose60D(self, pair, last_candle):
|
||||
if last_candle['close'] > last_candle['max12']:
|
||||
return 1
|
||||
if last_candle['close'] < last_candle['min12']:
|
||||
return 0
|
||||
return round(
|
||||
(last_candle['close'] - last_candle['min12']) / (last_candle['max12'] - last_candle['min12']), 4)
|
||||
|
||||
|
||||
def printLineLog(self):
|
||||
self.printLog(
|
||||
f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 12}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}{'-' * 9}+{'-' * 5}+{'-' * 7}+"
|
||||
f"{'-' * 3}"
|
||||
# "+{'-' * 3}+{'-' * 3}
|
||||
f"+{'-' * 6}+{'-' * 7}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+"
|
||||
)
|
||||
|
||||
def printLog(self, str):
|
||||
if self.config.get('runmode') == 'hyperopt' or self.dp.runmode.value in ('hyperopt'):
|
||||
return
|
||||
if not self.dp.runmode.value in ('backtest', 'hyperopt', 'lookahead-analysis'):
|
||||
logger.info(str)
|
||||
else:
|
||||
if not self.dp.runmode.value in ('hyperopt'):
|
||||
print(str)
|
||||
|
||||
def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None,
|
||||
stake=None,
|
||||
last_candle=None):
|
||||
# Couleurs ANSI de base
|
||||
RED = "\033[31m"
|
||||
GREEN = "\033[32m"
|
||||
YELLOW = "\033[33m"
|
||||
BLUE = "\033[34m"
|
||||
MAGENTA = "\033[35m"
|
||||
CYAN = "\033[36m"
|
||||
RESET = "\033[0m"
|
||||
|
||||
# Afficher les colonnes une seule fois
|
||||
if self.config.get('runmode') == 'hyperopt' or self.dp.runmode.value in ('hyperopt'):
|
||||
return
|
||||
if self.columns_logged % 10 == 0:
|
||||
self.printLog(
|
||||
f"| {'Date':<16} | {'Action':<10} |{'Pair':<5}| {'Trade Type':<18} |{'Rate':>8} | {'Dispo':>6} | {'Profit':>8} | {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}| {'last_max':>7}|{'Buys':>5}| {'Stake':>5} |"
|
||||
f"Tdc|{'val':>6}| RSI |s201d|s5_1d|s5_2d|s51h|s52h"
|
||||
)
|
||||
self.printLineLog()
|
||||
df = pd.DataFrame.from_dict(self.pairs, orient='index')
|
||||
colonnes_a_exclure = ['last_candle', 'last_trade', 'last_palier_index',
|
||||
'trade_info', 'last_date', 'expected_profit', 'last_count_of_buys',
|
||||
'base_stake_amount', 'stop_buy']
|
||||
df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
|
||||
# df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
|
||||
|
||||
print(df_filtered)
|
||||
|
||||
self.columns_logged += 1
|
||||
date = str(date)[:16] if date else "-"
|
||||
limit = None
|
||||
# if buys is not None:
|
||||
# limit = round(last_rate * (1 - self.fibo[buys] / 100), 4)
|
||||
|
||||
rsi = ''
|
||||
rsi_pct = ''
|
||||
sma5_1d = ''
|
||||
sma5_1h = ''
|
||||
|
||||
sma5 = str(sma5_1d) + ' ' + str(sma5_1h)
|
||||
|
||||
last_lost = '' #self.getLastLost(last_candle, pair)
|
||||
|
||||
if buys is None:
|
||||
buys = ''
|
||||
|
||||
max_touch = '' # round(last_candle['max12'], 1) #round(self.pairs[pair]['max_touch'], 1)
|
||||
pct_max = '' #self.getPctFirstBuy(pair, last_candle)
|
||||
|
||||
total_counts = str(buys) + '/' + str(
|
||||
sum(pair_data['count_of_buys'] for pair_data in self.pairs.values()))
|
||||
|
||||
dist_max = '' #self.getDistMax(last_candle, pair)
|
||||
val = 0 #self.getProbaHausseSma5d(last_candle)
|
||||
|
||||
pct60 = 0 #round(100 * self.getPct60D(pair, last_candle), 2)
|
||||
|
||||
color = GREEN if profit > 0 else RED
|
||||
# color_sma20 = GREEN if last_candle['sma20_deriv1'] > 0 else RED
|
||||
# color_sma5 = GREEN if last_candle['mid_smooth_5_deriv1'] > 0 else RED
|
||||
# color_sma5_2 = GREEN if last_candle['mid_smooth_5_deriv2'] > 0 else RED
|
||||
# color_sma5_1h = GREEN if last_candle['sma5_deriveriv1'] > 0 else RED
|
||||
# color_sma5_2h = GREEN if last_candle['sma5_deriveriv2'] > 0 else RED
|
||||
|
||||
last_max = int(self.pairs[pair]['last_max']) if self.pairs[pair]['last_max'] > 1 else round(
|
||||
self.pairs[pair]['last_max'], 3)
|
||||
last_min = int(self.pairs[pair]['last_min']) if self.pairs[pair]['last_min'] > 1 else round(
|
||||
self.pairs[pair]['last_min'], 3)
|
||||
|
||||
profit = str(profit) + '/' + str(round(self.pairs[pair]['max_profit'], 2))
|
||||
|
||||
# 🟢 Dérivée 1 > 0 et dérivée 2 > 0: tendance haussière qui s’accélère.
|
||||
# 🟡 Dérivée 1 > 0 et dérivée 2 < 0: tendance haussière qui ralentit → essoufflement potentiel.
|
||||
# 🔴 Dérivée 1 < 0 et dérivée 2 < 0: tendance baissière qui s’accélère.
|
||||
# 🟠 Dérivée 1 < 0 et dérivée 2 > 0: tendance baissière qui ralentit → possible bottom.
|
||||
|
||||
# tdc last_candle['tendency_12']
|
||||
self.printLog(
|
||||
f"| {date:<16} |{action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} |{rate or '-':>9}| {dispo or '-':>6} "
|
||||
f"|{color}{profit or '-':>10}{RESET}| {pct_max or '-':>6} | {round(self.pairs[pair]['max_touch'], 2) or '-':>11} | {last_lost or '-':>12} "
|
||||
f"| {last_max or '-':>7} | {last_min or '-':>7} |{total_counts or '-':>5}|{stake or '-':>7}"
|
||||
f"|{'-':>3}|"
|
||||
f"{round(val, 1) or '-' :>6}|"
|
||||
# f"{round(last_candle['rsi'], 0):>7}|{color_sma20}{round(last_candle['sma20_deriv1'], 2):>5}{RESET}"
|
||||
# f"|{color_sma5}{round(last_candle['mid_smooth_5_deriv1'], 2):>5}{RESET}|{color_sma5_2}{round(last_candle['mid_smooth_5_deriv2'], 2):>5}{RESET}"
|
||||
# f"|{color_sma5_1h}{round(last_candle['sma5_deriveriv1'], 2):>5}{RESET}|{color_sma5_2h}{round(last_candle['sma5_deriveriv2'], 2):>5}{RESET}"
|
||||
# f"|{last_candle['min60']}|{last_candle['max60']}"
|
||||
)
|
||||
@@ -1,7 +1,7 @@
|
||||
import numpy as np
|
||||
import talib.abstract as ta
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
import arrow
|
||||
# import arrow
|
||||
|
||||
from freqtrade.strategy import (IStrategy, merge_informative_pair, stoploss_from_open,
|
||||
IntParameter, DecimalParameter, CategoricalParameter)
|
||||
@@ -162,7 +162,7 @@ def SSLChannels(dataframe, length=10, mode='sma'):
|
||||
df['smaLow'] = df['low'].rolling(length).mean()
|
||||
|
||||
df['hlv'] = np.where(df['close'] > df['smaHigh'], 1,
|
||||
np.where(df['close'] < df['smaLow'], -1, np.NAN))
|
||||
np.where(df['close'] < df['smaLow'], -1, np.nan))
|
||||
df['hlv'] = df['hlv'].ffill()
|
||||
|
||||
df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
|
||||
@@ -180,7 +180,7 @@ def SSLChannels_ATR(dataframe, length=7):
|
||||
df['ATR'] = ta.ATR(df, timeperiod=14)
|
||||
df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
|
||||
df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
|
||||
df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
|
||||
df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.nan))
|
||||
df['hlv'] = df['hlv'].ffill()
|
||||
df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
|
||||
df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
|
||||
|
||||
@@ -164,7 +164,7 @@ class Supertrend(IStrategy):
|
||||
df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] >= df['final_lb'].iat[i] else \
|
||||
df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] < df['final_lb'].iat[i] else 0.00
|
||||
# Mark the trend direction up/down
|
||||
df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN)
|
||||
df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.nan)
|
||||
|
||||
# Remove basic and final bands from the columns
|
||||
df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)
|
||||
|
||||
@@ -487,10 +487,10 @@ class Zeus_5_1(IStrategy):
|
||||
def bot_loop_start(self, **kwargs) -> None:
|
||||
inf_tf = '5m'
|
||||
pairs = self.dp.current_whitelist()
|
||||
print("Calcul des pairs informatives")
|
||||
# print("Calcul des pairs informatives")
|
||||
for pairname in pairs:
|
||||
self.stop_buying[pairname] = True
|
||||
print("Fin Calcul des pairs informatives")
|
||||
# print("Fin Calcul des pairs informatives")
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
|
||||
current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
|
||||
|
||||
@@ -491,10 +491,10 @@ class Zeus_5_2(IStrategy):
|
||||
def bot_loop_start(self, **kwargs) -> None:
|
||||
inf_tf = '5m'
|
||||
pairs = self.dp.current_whitelist()
|
||||
print("Calcul des pairs informatives")
|
||||
# print("Calcul des pairs informatives")
|
||||
for pairname in pairs:
|
||||
self.stop_buying[pairname] = True
|
||||
print("Fin Calcul des pairs informatives")
|
||||
# print("Fin Calcul des pairs informatives")
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
|
||||
current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
|
||||
|
||||
@@ -487,10 +487,10 @@ class Zeus_6(IStrategy):
|
||||
def bot_loop_start(self, **kwargs) -> None:
|
||||
inf_tf = '5m'
|
||||
pairs = self.dp.current_whitelist()
|
||||
print("Calcul des pairs informatives")
|
||||
# print("Calcul des pairs informatives")
|
||||
for pairname in pairs:
|
||||
self.stop_buying[pairname] = True
|
||||
print("Fin Calcul des pairs informatives")
|
||||
# print("Fin Calcul des pairs informatives")
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
|
||||
current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
|
||||
|
||||
1610
Zeus_8_1d.py
Normal file
1610
Zeus_8_1d.py
Normal file
File diff suppressed because it is too large
Load Diff
376
Zeus_8_1d_Bilan.txt
Normal file
376
Zeus_8_1d_Bilan.txt
Normal file
@@ -0,0 +1,376 @@
|
||||
BACKTESTING REPORT
|
||||
┏━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ DOGE/USDT │ 20 │ 6.58 │ 234.716 │ 7.82 │ 12 days, 21:54:00 │ 20 0 0 100 │
|
||||
│ XRP/USDT │ 29 │ 4.96 │ 198.511 │ 6.62 │ 8 days, 17:43:00 │ 28 0 1 96.6 │
|
||||
│ SOL/USDT │ 23 │ 4.96 │ 159.580 │ 5.32 │ 11 days, 1:29:00 │ 22 0 1 95.7 │
|
||||
│ ETH/USDT │ 26 │ 4.12 │ 155.679 │ 5.19 │ 9 days, 20:48:00 │ 26 0 0 100 │
|
||||
│ BTC/USDT │ 23 │ 2.57 │ 76.047 │ 2.53 │ 10 days, 23:47:00 │ 22 0 1 95.7 │
|
||||
│ TOTAL │ 121 │ 4.59 │ 824.533 │ 27.48 │ 10 days, 12:59:00 │ 118 0 3 97.5 │
|
||||
└───────────┴────────┴──────────────┴─────────────────┴──────────────┴───────────────────┴────────────────────────┘
|
||||
LEFT OPEN TRADES REPORT
|
||||
┏━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ ETH/USDT │ 1 │ 3.07 │ 1.533 │ 0.05 │ 2 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ DOGE/USDT │ 1 │ 0.47 │ 0.741 │ 0.02 │ 2 days, 5:00:00 │ 1 0 0 100 │
|
||||
│ BTC/USDT │ 1 │ -0.13 │ -0.065 │ -0.0 │ 2 days, 7:00:00 │ 0 0 1 0 │
|
||||
│ SOL/USDT │ 1 │ -1.47 │ -0.917 │ -0.03 │ 19:00:00 │ 0 0 1 0 │
|
||||
│ XRP/USDT │ 1 │ -4.64 │ -11.192 │ -0.37 │ 54 days, 8:00:00 │ 0 0 1 0 │
|
||||
│ TOTAL │ 5 │ -0.54 │ -9.899 │ -0.33 │ 12 days, 9:00:00 │ 2 0 3 40.0 │
|
||||
└───────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
|
||||
ENTER TAG STATS
|
||||
┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ smth_12 │ 121 │ 4.59 │ 824.533 │ 27.48 │ 10 days, 12:59:00 │ 118 0 3 97.5 │
|
||||
│ TOTAL │ 121 │ 4.59 │ 824.533 │ 27.48 │ 10 days, 12:59:00 │ 118 0 3 97.5 │
|
||||
└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴───────────────────┴────────────────────────┘
|
||||
EXIT REASON STATS
|
||||
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ RSI_XRP_5_0_28.43 │ 1 │ 15.15 │ 71.078 │ 2.37 │ 15 days, 4:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_8_0_34.72 │ 1 │ 6.36 │ 53.262 │ 1.78 │ 125 days, 19:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_6_0_46.32 │ 1 │ 10.45 │ 52.528 │ 1.75 │ 47 days, 13:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_3_0_26.01 │ 1 │ 21.51 │ 34.730 │ 1.16 │ 6 days, 1:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_3_0_39.61 │ 1 │ 14.32 │ 34.062 │ 1.14 │ 11 days, 21:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_3_0_14.24 │ 1 │ 15.48 │ 26.538 │ 0.88 │ 11 days, 17:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_2_0_9.03 │ 1 │ 17.25 │ 26.223 │ 0.87 │ 8 days, 1:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_4_0_19.78 │ 1 │ 9.64 │ 21.919 │ 0.73 │ 9 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_3_0_31.46 │ 1 │ 10.39 │ 20.674 │ 0.69 │ 8 days, 13:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_4_0_24.35 │ 1 │ 6.46 │ 20.553 │ 0.69 │ 13 days, 8:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_4_0_29.59 │ 1 │ 6.68 │ 18.794 │ 0.63 │ 16 days, 21:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_2_0_23.33 │ 1 │ 17.89 │ 18.435 │ 0.61 │ 3 days, 20:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_4_0_34.21 │ 1 │ 7.03 │ 17.503 │ 0.58 │ 12 days, 1:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_3_0_18.45 │ 1 │ 10.19 │ 16.852 │ 0.56 │ 7 days, 0:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_8_0_37.83 │ 1 │ 3.12 │ 16.616 │ 0.55 │ 79 days, 11:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_4_0_12.14 │ 1 │ 6.86 │ 15.212 │ 0.51 │ 16 days, 15:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_1_0_19.08 │ 1 │ 18.77 │ 14.063 │ 0.47 │ 4 days, 11:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_20.83 │ 1 │ 26.55 │ 13.279 │ 0.44 │ 3 days, 16:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_3_0_19.63 │ 1 │ 7.04 │ 13.133 │ 0.44 │ 14 days, 0:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_3_0_25.43 │ 1 │ 6.43 │ 12.901 │ 0.43 │ 12 days, 17:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_1_0_27.27 │ 1 │ 12.06 │ 12.054 │ 0.4 │ 2 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_2_0_67.31 │ 1 │ 11.51 │ 11.807 │ 0.39 │ 3 days, 13:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_3_0_43.54 │ 1 │ 5.33 │ 11.803 │ 0.39 │ 46 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_1_0_11.11 │ 1 │ 12.28 │ 11.253 │ 0.38 │ 3 days, 16:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_4_0_54.81 │ 1 │ 4.36 │ 10.812 │ 0.36 │ 13 days, 15:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_34.78 │ 1 │ 16.81 │ 10.484 │ 0.35 │ 2 days, 20:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_3_0_26.24 │ 1 │ 6.2 │ 10.401 │ 0.35 │ 7 days, 23:00:00 │ 1 0 0 100 │
|
||||
│ RSI_ETH_1_0_8.93 │ 1 │ 20.52 │ 10.253 │ 0.34 │ 4 days, 22:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_3_0_32.89 │ 1 │ 6.37 │ 10.247 │ 0.34 │ 6 days, 18:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_2_0_28.89 │ 1 │ 9.34 │ 9.561 │ 0.32 │ 5 days, 14:00:00 │ 1 0 0 100 │
|
||||
│ RSI_SOL_4_0_40.62 │ 1 │ 2.98 │ 9.471 │ 0.32 │ 10 days, 23:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_3_0_69.84 │ 1 │ 2.64 │ 9.241 │ 0.31 │ 3 days, 14:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_3_0_60.36 │ 1 │ 4.38 │ 8.823 │ 0.29 │ 7 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_2_0_47.83 │ 1 │ 8.04 │ 8.420 │ 0.28 │ 3 days, 23:00:00 │ 1 0 0 100 │
|
||||
│ RSI_XRP_1_0_9.78 │ 1 │ 13.26 │ 8.291 │ 0.28 │ 1 day, 10:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_33.33 │ 1 │ 15.27 │ 7.630 │ 0.25 │ 3 days, 20:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_1_0_12.35 │ 1 │ 14.27 │ 7.136 │ 0.24 │ 3 days, 22:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_1_0_39.42 │ 1 │ 10.17 │ 6.354 │ 0.21 │ 3 days, 15:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_4_0_17.57 │ 1 │ 2.59 │ 6.081 │ 0.2 │ 15 days, 18:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_2_0_18.06 │ 1 │ 5.89 │ 5.956 │ 0.2 │ 9 days, 11:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_1_0_17.14 │ 1 │ 9.45 │ 5.834 │ 0.19 │ 8 days, 2:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_8_0_81.16 │ 1 │ 0.73 │ 5.508 │ 0.18 │ 107 days, 19:00:00 │ 1 0 0 100 │
|
||||
│ RSI_BTC_5_0_31.25 │ 1 │ 1.93 │ 5.464 │ 0.18 │ 17 days, 20:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_6_0_30.26 │ 1 │ 1.51 │ 5.343 │ 0.18 │ 31 days, 22:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_4_0_83.45 │ 1 │ 1.65 │ 4.730 │ 0.16 │ 11 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_26.98 │ 1 │ 9.3 │ 4.588 │ 0.15 │ 4 days, 12:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_1_0_25.0 │ 1 │ 7.26 │ 4.538 │ 0.15 │ 3 days, 7:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_1_0_20.0 │ 1 │ 8.86 │ 4.432 │ 0.15 │ 5 days, 22:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_5_0_68.35 │ 1 │ 1.23 │ 4.368 │ 0.15 │ 55 days, 19:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_1_0_10.64 │ 1 │ 8.42 │ 4.189 │ 0.14 │ 6 days, 23:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_3_0_60.19 │ 1 │ 2.61 │ 4.110 │ 0.14 │ 4 days, 3:00:00 │ 1 0 0 100 │
|
||||
│ RSI_ETH_3_0_38.98 │ 1 │ 2.1 │ 3.591 │ 0.12 │ 12 days, 8:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_1_0_27.66 │ 1 │ 5.48 │ 3.425 │ 0.11 │ 2 days, 16:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_2_0_43.86 │ 1 │ 2.49 │ 3.220 │ 0.11 │ 4 days, 1:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_1_0_40.74 │ 1 │ 5.16 │ 3.215 │ 0.11 │ 2 days, 2:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_1_0_50.77 │ 1 │ 5.12 │ 3.201 │ 0.11 │ 2 days, 12:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_7_0_96.68 │ 1 │ 0.29 │ 3.008 │ 0.1 │ 110 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_5_0_82.93 │ 1 │ 0.66 │ 2.808 │ 0.09 │ 38 days, 21:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_3_0_74.07 │ 1 │ 1.13 │ 2.788 │ 0.09 │ 6 days, 14:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_3_0_77.5 │ 1 │ 1.35 │ 2.745 │ 0.09 │ 2 days, 16:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_2_0_57.63 │ 1 │ 2.33 │ 2.489 │ 0.08 │ 5 days, 9:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_3_0_46.67 │ 1 │ 1.18 │ 2.404 │ 0.08 │ 4 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_54.0 │ 1 │ 4.34 │ 2.338 │ 0.08 │ 3 days, 3:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_4_0_76.04 │ 1 │ 0.96 │ 2.254 │ 0.08 │ 13 days, 17:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_1_0_56.0 │ 1 │ 3.55 │ 2.217 │ 0.07 │ 6 days, 15:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_45.95 │ 1 │ 3.99 │ 1.993 │ 0.07 │ 1 day, 5:00:00 │ 1 0 0 100 │
|
||||
│ RSI_ETH_1_0_16.67 │ 1 │ 3.97 │ 1.977 │ 0.07 │ 14:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_1_0_26.09 │ 1 │ 2.8 │ 1.746 │ 0.06 │ 2 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_2_0_71.93 │ 1 │ 1.52 │ 1.583 │ 0.05 │ 2 days, 11:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_1_0_11.11 │ 1 │ 3.14 │ 1.565 │ 0.05 │ 2 days, 8:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_3_0_27.27 │ 1 │ 0.85 │ 1.557 │ 0.05 │ 12:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_4_0_79.73 │ 1 │ 0.65 │ 1.509 │ 0.05 │ 6 days, 20:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_2_0_76.56 │ 1 │ 1.15 │ 1.466 │ 0.05 │ 3 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_1_0_75.0 │ 1 │ 1.75 │ 1.314 │ 0.04 │ 3 days, 14:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_3_0_73.47 │ 1 │ 0.76 │ 1.289 │ 0.04 │ 20 days, 8:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_27.78 │ 1 │ 1.27 │ 1.268 │ 0.04 │ 6:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_2_0_33.33 │ 1 │ 0.95 │ 1.203 │ 0.04 │ 1 day, 1:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_2_0_83.58 │ 1 │ 1.12 │ 1.148 │ 0.04 │ 1 day, 16:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_1_0_54.17 │ 1 │ 2.19 │ 1.097 │ 0.04 │ 1 day, 15:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_2_0_44.44 │ 1 │ 0.79 │ 1.033 │ 0.03 │ 9 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_1_0_77.78 │ 1 │ 2.0 │ 0.999 │ 0.03 │ 1 day, 16:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_1_0_41.18 │ 1 │ 1.93 │ 0.964 │ 0.03 │ 5 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_3_0_85.94 │ 1 │ 0.48 │ 0.944 │ 0.03 │ 14 days, 16:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_3_0_12.5 │ 1 │ 0.45 │ 0.712 │ 0.02 │ 1 day, 1:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_81.58 │ 1 │ 1.12 │ 0.697 │ 0.02 │ 2 days, 7:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_1_0_30.0 │ 1 │ 1.1 │ 0.688 │ 0.02 │ 10:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_3_0_30.0 │ 1 │ 0.42 │ 0.669 │ 0.02 │ 1 day, 6:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_1_0_0.0 │ 1 │ 1.07 │ 0.666 │ 0.02 │ 4:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_2_0_72.73 │ 1 │ 0.62 │ 0.631 │ 0.02 │ 1 day, 20:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_2_0_25.0 │ 1 │ 0.6 │ 0.614 │ 0.02 │ 13:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_2_0_66.67 │ 1 │ 0.59 │ 0.607 │ 0.02 │ 4 days, 22:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_1_0_57.14 │ 2 │ 0.38 │ 0.590 │ 0.02 │ 8:00:00 │ 2 0 0 100 │
|
||||
│ Drv3_SOL_2_0_64.71 │ 1 │ 0.57 │ 0.588 │ 0.02 │ 1 day, 12:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_2_0_94.95 │ 1 │ 0.36 │ 0.548 │ 0.02 │ 2 days, 5:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_28.57 │ 1 │ 0.87 │ 0.545 │ 0.02 │ 3:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_2_0_28.57 │ 1 │ 0.49 │ 0.506 │ 0.02 │ 1 day, 5:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_2_0_37.5 │ 1 │ 0.45 │ 0.489 │ 0.02 │ 3 days, 2:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_1_0_73.68 │ 1 │ 0.94 │ 0.466 │ 0.02 │ 2 days, 18:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_2_0_76.47 │ 1 │ 0.38 │ 0.397 │ 0.01 │ 15 days, 3:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_1_0_72.73 │ 1 │ 0.7 │ 0.347 │ 0.01 │ 1 day, 23:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_1_0_57.14 │ 1 │ 0.64 │ 0.318 │ 0.01 │ 1 day, 14:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_85.0 │ 1 │ 0.48 │ 0.298 │ 0.01 │ 2 days, 7:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_1_0_76.92 │ 1 │ 0.58 │ 0.290 │ 0.01 │ 2 days, 1:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_1_0_75.0 │ 1 │ 0.45 │ 0.223 │ 0.01 │ 1 day, 15:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_DOGE_1_0_87.5 │ 1 │ 0.32 │ 0.198 │ 0.01 │ 1 day, 11:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_80.0 │ 1 │ 0.32 │ 0.196 │ 0.01 │ 1 day, 14:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_2_0_94.59 │ 1 │ 0.19 │ 0.188 │ 0.01 │ 6 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_92.0 │ 1 │ 0.35 │ 0.173 │ 0.01 │ 3 days, 2:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_XRP_1_0_92.86 │ 1 │ 0.29 │ 0.147 │ 0.0 │ 1 day, 18:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_2_0_97.62 │ 1 │ 0.14 │ 0.145 │ 0.0 │ 1 day, 14:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_ETH_2_0_91.67 │ 1 │ 0.14 │ 0.143 │ 0.0 │ 1 day, 0:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_1_0_85.71 │ 1 │ 0.27 │ 0.133 │ 0.0 │ 2 days, 11:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_SOL_1_0_80.0 │ 1 │ 0.23 │ 0.114 │ 0.0 │ 2 days, 0:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_2_0_66.67 │ 1 │ 0.11 │ 0.107 │ 0.0 │ 1 day, 0:00:00 │ 1 0 0 100 │
|
||||
│ Drv3_BTC_1_0_90.0 │ 1 │ 0.21 │ 0.104 │ 0.0 │ 1 day, 9:00:00 │ 1 0 0 100 │
|
||||
│ force_exit │ 5 │ -0.54 │ -9.899 │ -0.33 │ 12 days, 9:00:00 │ 2 0 3 40.0 │
|
||||
│ TOTAL │ 121 │ 4.59 │ 824.533 │ 27.48 │ 10 days, 12:59:00 │ 118 0 3 97.5 │
|
||||
└─────────────────────┴───────┴──────────────┴─────────────────┴──────────────┴────────────────────┴────────────────────────┘
|
||||
MIXED TAG STATS
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ ('smth_12', 'RSI_XRP_5_0_28.43') │ │ 1 │ 15.15 │ 71.078 │ 2.37 │ 15 days, 4:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_8_0_34.72') │ │ 1 │ 6.36 │ 53.262 │ 1.78 │ 125 days, 19:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_6_0_46.32') │ │ 1 │ 10.45 │ 52.528 │ 1.75 │ 47 days, 13:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_3_0_26.01') │ │ 1 │ 21.51 │ 34.730 │ 1.16 │ 6 days, 1:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_3_0_39.61') │ │ 1 │ 14.32 │ 34.062 │ 1.14 │ 11 days, 21:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_3_0_14.24') │ │ 1 │ 15.48 │ 26.538 │ 0.88 │ 11 days, 17:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_2_0_9.03') │ │ 1 │ 17.25 │ 26.223 │ 0.87 │ 8 days, 1:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_4_0_19.78') │ │ 1 │ 9.64 │ 21.919 │ 0.73 │ 9 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_3_0_31.46') │ │ 1 │ 10.39 │ 20.674 │ 0.69 │ 8 days, 13:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_4_0_24.35') │ │ 1 │ 6.46 │ 20.553 │ 0.69 │ 13 days, 8:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_4_0_29.59') │ │ 1 │ 6.68 │ 18.794 │ 0.63 │ 16 days, 21:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_2_0_23.33') │ │ 1 │ 17.89 │ 18.435 │ 0.61 │ 3 days, 20:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_4_0_34.21') │ │ 1 │ 7.03 │ 17.503 │ 0.58 │ 12 days, 1:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_3_0_18.45') │ │ 1 │ 10.19 │ 16.852 │ 0.56 │ 7 days, 0:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_8_0_37.83') │ │ 1 │ 3.12 │ 16.616 │ 0.55 │ 79 days, 11:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_4_0_12.14') │ │ 1 │ 6.86 │ 15.212 │ 0.51 │ 16 days, 15:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_1_0_19.08') │ │ 1 │ 18.77 │ 14.063 │ 0.47 │ 4 days, 11:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_20.83') │ │ 1 │ 26.55 │ 13.279 │ 0.44 │ 3 days, 16:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_3_0_19.63') │ │ 1 │ 7.04 │ 13.133 │ 0.44 │ 14 days, 0:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_3_0_25.43') │ │ 1 │ 6.43 │ 12.901 │ 0.43 │ 12 days, 17:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_1_0_27.27') │ │ 1 │ 12.06 │ 12.054 │ 0.4 │ 2 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_2_0_67.31') │ │ 1 │ 11.51 │ 11.807 │ 0.39 │ 3 days, 13:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_3_0_43.54') │ │ 1 │ 5.33 │ 11.803 │ 0.39 │ 46 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_1_0_11.11') │ │ 1 │ 12.28 │ 11.253 │ 0.38 │ 3 days, 16:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_4_0_54.81') │ │ 1 │ 4.36 │ 10.812 │ 0.36 │ 13 days, 15:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_34.78') │ │ 1 │ 16.81 │ 10.484 │ 0.35 │ 2 days, 20:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_3_0_26.24') │ │ 1 │ 6.2 │ 10.401 │ 0.35 │ 7 days, 23:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'RSI_ETH_1_0_8.93') │ │ 1 │ 20.52 │ 10.253 │ 0.34 │ 4 days, 22:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_3_0_32.89') │ │ 1 │ 6.37 │ 10.247 │ 0.34 │ 6 days, 18:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_2_0_28.89') │ │ 1 │ 9.34 │ 9.561 │ 0.32 │ 5 days, 14:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'RSI_SOL_4_0_40.62') │ │ 1 │ 2.98 │ 9.471 │ 0.32 │ 10 days, 23:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_3_0_69.84') │ │ 1 │ 2.64 │ 9.241 │ 0.31 │ 3 days, 14:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_3_0_60.36') │ │ 1 │ 4.38 │ 8.823 │ 0.29 │ 7 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_2_0_47.83') │ │ 1 │ 8.04 │ 8.420 │ 0.28 │ 3 days, 23:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'RSI_XRP_1_0_9.78') │ │ 1 │ 13.26 │ 8.291 │ 0.28 │ 1 day, 10:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_33.33') │ │ 1 │ 15.27 │ 7.630 │ 0.25 │ 3 days, 20:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_1_0_12.35') │ │ 1 │ 14.27 │ 7.136 │ 0.24 │ 3 days, 22:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_1_0_39.42') │ │ 1 │ 10.17 │ 6.354 │ 0.21 │ 3 days, 15:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_4_0_17.57') │ │ 1 │ 2.59 │ 6.081 │ 0.2 │ 15 days, 18:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_2_0_18.06') │ │ 1 │ 5.89 │ 5.956 │ 0.2 │ 9 days, 11:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_1_0_17.14') │ │ 1 │ 9.45 │ 5.834 │ 0.19 │ 8 days, 2:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_8_0_81.16') │ │ 1 │ 0.73 │ 5.508 │ 0.18 │ 107 days, 19:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'RSI_BTC_5_0_31.25') │ │ 1 │ 1.93 │ 5.464 │ 0.18 │ 17 days, 20:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_6_0_30.26') │ │ 1 │ 1.51 │ 5.343 │ 0.18 │ 31 days, 22:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_4_0_83.45') │ │ 1 │ 1.65 │ 4.730 │ 0.16 │ 11 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_26.98') │ │ 1 │ 9.3 │ 4.588 │ 0.15 │ 4 days, 12:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_1_0_25.0') │ │ 1 │ 7.26 │ 4.538 │ 0.15 │ 3 days, 7:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_1_0_20.0') │ │ 1 │ 8.86 │ 4.432 │ 0.15 │ 5 days, 22:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_5_0_68.35') │ │ 1 │ 1.23 │ 4.368 │ 0.15 │ 55 days, 19:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_1_0_10.64') │ │ 1 │ 8.42 │ 4.189 │ 0.14 │ 6 days, 23:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_3_0_60.19') │ │ 1 │ 2.61 │ 4.110 │ 0.14 │ 4 days, 3:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'RSI_ETH_3_0_38.98') │ │ 1 │ 2.1 │ 3.591 │ 0.12 │ 12 days, 8:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_1_0_27.66') │ │ 1 │ 5.48 │ 3.425 │ 0.11 │ 2 days, 16:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_2_0_43.86') │ │ 1 │ 2.49 │ 3.220 │ 0.11 │ 4 days, 1:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_1_0_40.74') │ │ 1 │ 5.16 │ 3.215 │ 0.11 │ 2 days, 2:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_1_0_50.77') │ │ 1 │ 5.12 │ 3.201 │ 0.11 │ 2 days, 12:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_7_0_96.68') │ │ 1 │ 0.29 │ 3.008 │ 0.1 │ 110 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_5_0_82.93') │ │ 1 │ 0.66 │ 2.808 │ 0.09 │ 38 days, 21:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_3_0_74.07') │ │ 1 │ 1.13 │ 2.788 │ 0.09 │ 6 days, 14:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_3_0_77.5') │ │ 1 │ 1.35 │ 2.745 │ 0.09 │ 2 days, 16:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_2_0_57.63') │ │ 1 │ 2.33 │ 2.489 │ 0.08 │ 5 days, 9:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_3_0_46.67') │ │ 1 │ 1.18 │ 2.404 │ 0.08 │ 4 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_54.0') │ │ 1 │ 4.34 │ 2.338 │ 0.08 │ 3 days, 3:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_4_0_76.04') │ │ 1 │ 0.96 │ 2.254 │ 0.08 │ 13 days, 17:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_1_0_56.0') │ │ 1 │ 3.55 │ 2.217 │ 0.07 │ 6 days, 15:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_45.95') │ │ 1 │ 3.99 │ 1.993 │ 0.07 │ 1 day, 5:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'RSI_ETH_1_0_16.67') │ │ 1 │ 3.97 │ 1.977 │ 0.07 │ 14:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_1_0_26.09') │ │ 1 │ 2.8 │ 1.746 │ 0.06 │ 2 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_2_0_71.93') │ │ 1 │ 1.52 │ 1.583 │ 0.05 │ 2 days, 11:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_1_0_11.11') │ │ 1 │ 3.14 │ 1.565 │ 0.05 │ 2 days, 8:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_3_0_27.27') │ │ 1 │ 0.85 │ 1.557 │ 0.05 │ 12:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_4_0_79.73') │ │ 1 │ 0.65 │ 1.509 │ 0.05 │ 6 days, 20:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_2_0_76.56') │ │ 1 │ 1.15 │ 1.466 │ 0.05 │ 3 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_1_0_75.0') │ │ 1 │ 1.75 │ 1.314 │ 0.04 │ 3 days, 14:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_3_0_73.47') │ │ 1 │ 0.76 │ 1.289 │ 0.04 │ 20 days, 8:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_27.78') │ │ 1 │ 1.27 │ 1.268 │ 0.04 │ 6:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_2_0_33.33') │ │ 1 │ 0.95 │ 1.203 │ 0.04 │ 1 day, 1:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_2_0_83.58') │ │ 1 │ 1.12 │ 1.148 │ 0.04 │ 1 day, 16:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_1_0_54.17') │ │ 1 │ 2.19 │ 1.097 │ 0.04 │ 1 day, 15:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_2_0_44.44') │ │ 1 │ 0.79 │ 1.033 │ 0.03 │ 9 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_1_0_77.78') │ │ 1 │ 2.0 │ 0.999 │ 0.03 │ 1 day, 16:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_1_0_41.18') │ │ 1 │ 1.93 │ 0.964 │ 0.03 │ 5 days, 6:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_3_0_85.94') │ │ 1 │ 0.48 │ 0.944 │ 0.03 │ 14 days, 16:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_3_0_12.5') │ │ 1 │ 0.45 │ 0.712 │ 0.02 │ 1 day, 1:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_81.58') │ │ 1 │ 1.12 │ 0.697 │ 0.02 │ 2 days, 7:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_1_0_30.0') │ │ 1 │ 1.1 │ 0.688 │ 0.02 │ 10:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_3_0_30.0') │ │ 1 │ 0.42 │ 0.669 │ 0.02 │ 1 day, 6:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_1_0_0.0') │ │ 1 │ 1.07 │ 0.666 │ 0.02 │ 4:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_2_0_72.73') │ │ 1 │ 0.62 │ 0.631 │ 0.02 │ 1 day, 20:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_2_0_25.0') │ │ 1 │ 0.6 │ 0.614 │ 0.02 │ 13:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_2_0_66.67') │ │ 1 │ 0.59 │ 0.607 │ 0.02 │ 4 days, 22:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_1_0_57.14') │ │ 2 │ 0.38 │ 0.590 │ 0.02 │ 8:00:00 │ 2 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_2_0_64.71') │ │ 1 │ 0.57 │ 0.588 │ 0.02 │ 1 day, 12:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_2_0_94.95') │ │ 1 │ 0.36 │ 0.548 │ 0.02 │ 2 days, 5:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_28.57') │ │ 1 │ 0.87 │ 0.545 │ 0.02 │ 3:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_2_0_28.57') │ │ 1 │ 0.49 │ 0.506 │ 0.02 │ 1 day, 5:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_2_0_37.5') │ │ 1 │ 0.45 │ 0.489 │ 0.02 │ 3 days, 2:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_1_0_73.68') │ │ 1 │ 0.94 │ 0.466 │ 0.02 │ 2 days, 18:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_2_0_76.47') │ │ 1 │ 0.38 │ 0.397 │ 0.01 │ 15 days, 3:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_1_0_72.73') │ │ 1 │ 0.7 │ 0.347 │ 0.01 │ 1 day, 23:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_1_0_57.14') │ │ 1 │ 0.64 │ 0.318 │ 0.01 │ 1 day, 14:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_85.0') │ │ 1 │ 0.48 │ 0.298 │ 0.01 │ 2 days, 7:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_1_0_76.92') │ │ 1 │ 0.58 │ 0.290 │ 0.01 │ 2 days, 1:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_1_0_75.0') │ │ 1 │ 0.45 │ 0.223 │ 0.01 │ 1 day, 15:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_DOGE_1_0_87.5') │ │ 1 │ 0.32 │ 0.198 │ 0.01 │ 1 day, 11:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_80.0') │ │ 1 │ 0.32 │ 0.196 │ 0.01 │ 1 day, 14:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_2_0_94.59') │ │ 1 │ 0.19 │ 0.188 │ 0.01 │ 6 days, 10:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_92.0') │ │ 1 │ 0.35 │ 0.173 │ 0.01 │ 3 days, 2:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_XRP_1_0_92.86') │ │ 1 │ 0.29 │ 0.147 │ 0.0 │ 1 day, 18:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_2_0_97.62') │ │ 1 │ 0.14 │ 0.145 │ 0.0 │ 1 day, 14:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_ETH_2_0_91.67') │ │ 1 │ 0.14 │ 0.143 │ 0.0 │ 1 day, 0:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_1_0_85.71') │ │ 1 │ 0.27 │ 0.133 │ 0.0 │ 2 days, 11:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_SOL_1_0_80.0') │ │ 1 │ 0.23 │ 0.114 │ 0.0 │ 2 days, 0:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_2_0_66.67') │ │ 1 │ 0.11 │ 0.107 │ 0.0 │ 1 day, 0:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'Drv3_BTC_1_0_90.0') │ │ 1 │ 0.21 │ 0.104 │ 0.0 │ 1 day, 9:00:00 │ 1 0 0 100 │
|
||||
│ ('smth_12', 'force_exit') │ │ 5 │ -0.54 │ -9.899 │ -0.33 │ 12 days, 9:00:00 │ 2 0 3 40.0 │
|
||||
│ TOTAL │ │ 121 │ 4.59 │ 824.533 │ 27.48 │ 10 days, 12:59:00 │ 118 0 3 97.5 │
|
||||
└────────────────────────────────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴────────────────────┴────────────────────────┘
|
||||
WEEK BREAKDOWN
|
||||
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━┳━━━━━━━┳━━━━━━━━┓
|
||||
┃ Week ┃ Tot Profit USDT ┃ Wins ┃ Draws ┃ Losses ┃
|
||||
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━╇━━━━━━━╇━━━━━━━━┩
|
||||
│ 06/01/2025 │ 37.217 │ 6 │ 0 │ 0 │
|
||||
│ 13/01/2025 │ 12.464 │ 2 │ 0 │ 0 │
|
||||
│ 20/01/2025 │ 76.722 │ 7 │ 0 │ 0 │
|
||||
│ 27/01/2025 │ 4.684 │ 4 │ 0 │ 0 │
|
||||
│ 03/02/2025 │ 3.577 │ 4 │ 0 │ 0 │
|
||||
│ 10/02/2025 │ 0 │ 0 │ 0 │ 0 │
|
||||
│ 17/02/2025 │ 17.503 │ 1 │ 0 │ 0 │
|
||||
│ 24/02/2025 │ 0 │ 0 │ 0 │ 0 │
|
||||
│ 03/03/2025 │ 71.078 │ 1 │ 0 │ 0 │
|
||||
│ 10/03/2025 │ 9.241 │ 1 │ 0 │ 0 │
|
||||
│ 17/03/2025 │ 4.588 │ 1 │ 0 │ 0 │
|
||||
│ 24/03/2025 │ 2.338 │ 1 │ 0 │ 0 │
|
||||
│ 31/03/2025 │ 0.147 │ 1 │ 0 │ 0 │
|
||||
│ 07/04/2025 │ 0 │ 0 │ 0 │ 0 │
|
||||
│ 14/04/2025 │ 0 │ 0 │ 0 │ 0 │
|
||||
│ 21/04/2025 │ 0 │ 0 │ 0 │ 0 │
|
||||
│ 28/04/2025 │ 16.839 │ 2 │ 0 │ 0 │
|
||||
│ 05/05/2025 │ 0.964 │ 1 │ 0 │ 0 │
|
||||
│ 12/05/2025 │ 78.013 │ 5 │ 0 │ 0 │
|
||||
│ 19/05/2025 │ 6.222 │ 4 │ 0 │ 0 │
|
||||
│ 26/05/2025 │ 19.509 │ 3 │ 0 │ 0 │
|
||||
│ 02/06/2025 │ 0.944 │ 1 │ 0 │ 0 │
|
||||
│ 09/06/2025 │ 0 │ 0 │ 0 │ 0 │
|
||||
│ 16/06/2025 │ 9.2 │ 3 │ 0 │ 0 │
|
||||
│ 23/06/2025 │ 2.131 │ 2 │ 0 │ 0 │
|
||||
│ 30/06/2025 │ 7.723 │ 3 │ 0 │ 0 │
|
||||
│ 07/07/2025 │ 5.149 │ 5 │ 0 │ 0 │
|
||||
│ 14/07/2025 │ 81.049 │ 8 │ 0 │ 0 │
|
||||
│ 21/07/2025 │ 66.176 │ 5 │ 0 │ 0 │
|
||||
│ 28/07/2025 │ 27.276 │ 10 │ 0 │ 0 │
|
||||
│ 04/08/2025 │ 0.788 │ 2 │ 0 │ 0 │
|
||||
│ 11/08/2025 │ 62.555 │ 5 │ 0 │ 0 │
|
||||
│ 18/08/2025 │ 15.647 │ 6 │ 0 │ 0 │
|
||||
│ 25/08/2025 │ 47.927 │ 3 │ 0 │ 0 │
|
||||
│ 01/09/2025 │ 3.201 │ 1 │ 0 │ 0 │
|
||||
│ 08/09/2025 │ 3.22 │ 1 │ 0 │ 0 │
|
||||
│ 15/09/2025 │ 70.772 │ 6 │ 0 │ 0 │
|
||||
│ 22/09/2025 │ 6.969 │ 5 │ 0 │ 0 │
|
||||
│ 29/09/2025 │ 0 │ 0 │ 0 │ 0 │
|
||||
│ 06/10/2025 │ 62.599 │ 6 │ 0 │ 0 │
|
||||
│ 13/10/2025 │ -9.899 │ 2 │ 0 │ 3 │
|
||||
└────────────┴─────────────────┴──────┴───────┴────────┘
|
||||
SUMMARY METRICS
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Metric ┃ Value ┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ Backtesting from │ 2025-01-01 00:00:00 │
|
||||
│ Backtesting to │ 2025-10-07 10:00:00 │
|
||||
│ Trading Mode │ Spot │
|
||||
│ Max open trades │ 5 │
|
||||
│ │ │
|
||||
│ Total/Daily Avg Trades │ 121 / 0.43 │
|
||||
│ Starting balance │ 3000 USDT │
|
||||
│ Final balance │ 3824.533 USDT │
|
||||
│ Absolute profit │ 824.533 USDT │
|
||||
│ Total profit % │ 27.48% │
|
||||
│ CAGR % │ 37.39% │
|
||||
│ Sortino │ 11.17 │
|
||||
│ Sharpe │ 5.08 │
|
||||
│ Calmar │ 596.19 │
|
||||
│ SQN │ 6.71 │
|
||||
│ Profit factor │ 68.73 │
|
||||
│ Expectancy (Ratio) │ 6.81 (1.68) │
|
||||
│ Avg. daily profit % │ 0.10% │
|
||||
│ Avg. stake amount │ 155.858 USDT │
|
||||
│ Total trade volume │ 38619.369 USDT │
|
||||
│ │ │
|
||||
│ Best Pair │ XRP/USDT 6.62% │
|
||||
│ Worst Pair │ BTC/USDT 2.53% │
|
||||
│ Best trade │ XRP/USDT 26.55% │
|
||||
│ Worst trade │ XRP/USDT -4.64% │
|
||||
│ Best day │ 76.022 USDT │
|
||||
│ Worst day │ -9.899 USDT │
|
||||
│ Days win/draw/lose │ 75 / 203 / 1 │
|
||||
│ Avg. Duration Winners │ 10 days, 7:44:00 │
|
||||
│ Avg. Duration Loser │ 19 days, 3:20:00 │
|
||||
│ Max Consecutive Wins / Loss │ 116 / 2 │
|
||||
│ Rejected Entry signals │ 0 │
|
||||
│ Entry/Exit Timeouts │ 0 / 0 │
|
||||
│ │ │
|
||||
│ Min balance │ 3008.291 USDT │
|
||||
│ Max balance │ 3835.901 USDT │
|
||||
│ Max % of account underwater │ 0.32% │
|
||||
│ Absolute Drawdown (Account) │ 0.32% │
|
||||
│ Absolute Drawdown │ 12.109 USDT │
|
||||
│ Drawdown high │ 835.901 USDT │
|
||||
│ Drawdown low │ 823.792 USDT │
|
||||
│ Drawdown Start │ 2025-10-07 10:00:00 │
|
||||
│ Drawdown End │ 2025-10-07 10:00:00 │
|
||||
│ Market change │ 2.17% │
|
||||
└─────────────────────────────┴─────────────────────┘
|
||||
|
||||
Backtested 2025-01-01 00:00:00 -> 2025-10-07 10:00:00 | Max open trades : 5
|
||||
STRATEGY SUMMARY
|
||||
┏━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃
|
||||
┡━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ Zeus_8_1d │ 121 │ 4.59 │ 824.533 │ 27.48 │ 10 days, 12:59:00 │ 118 0 3 97.5 │ 12.109 USDT 0.32% │
|
||||
└───────────┴────────┴──────────────┴─────────────────┴──────────────┴───────────────────┴────────────────────────┴────────────────────┘
|
||||
@@ -8,25 +8,25 @@
|
||||
"stoploss": -1.0
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": false,
|
||||
"trailing_stop_positive": 0.254,
|
||||
"trailing_stop_positive_offset": 0.323,
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.148,
|
||||
"trailing_stop_positive_offset": 0.218,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 80
|
||||
"max_open_trades": 20
|
||||
},
|
||||
"buy": {
|
||||
"buy_horizon_predict_1h": 2
|
||||
},
|
||||
"sell": {
|
||||
"sell_allow_decrease": 0.2
|
||||
},
|
||||
"protection": {
|
||||
"protection_fibo": 9,
|
||||
"protection_percent_buy_lost": 3
|
||||
"mise_factor_buy": 0.01,
|
||||
"pct": 0.029,
|
||||
"pct_inc": 0.0027,
|
||||
"buy_filter_indicator": "rsi5_deriv1_1d",
|
||||
"buy_indicator0": "rsi60_inv_hau",
|
||||
"buy_indicator1": "mid_smooth_48_inv_bas",
|
||||
"buy_indicator2": "sma24_inv_bas_1h",
|
||||
"buy_indicator3": "bb_lowerband3_inv_hau_1h"
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-05-24 18:29:22.477903+00:00"
|
||||
"export_time": "2026-02-18 16:50:53.773582+00:00"
|
||||
}
|
||||
3753
Zeus_8_3_2_B_4_2.py
3753
Zeus_8_3_2_B_4_2.py
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
35
Zeus_LGBMRegressor.json
Normal file
35
Zeus_LGBMRegressor.json
Normal file
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"strategy_name": "Zeus_LGBMRegressor",
|
||||
"params": {
|
||||
"roi": {
|
||||
"0": 10
|
||||
},
|
||||
"stoploss": {
|
||||
"stoploss": -1
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.15,
|
||||
"trailing_stop_positive_offset": 0.2,
|
||||
"trailing_only_offset_is_reached": true
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 80
|
||||
},
|
||||
"buy": {
|
||||
"mise_factor_buy": 0.02,
|
||||
"mises": 2,
|
||||
"ml_prob_buy": -0.25,
|
||||
"pct": 0.012,
|
||||
"pct_inc": 0.001
|
||||
},
|
||||
"sell": {
|
||||
"ml_prob_sell": 0.07
|
||||
},
|
||||
"protection": {
|
||||
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-11-14 21:34:28.681343+00:00"
|
||||
}
|
||||
106
Zeus_LGBMRegressor.md
Normal file
106
Zeus_LGBMRegressor.md
Normal file
@@ -0,0 +1,106 @@
|
||||
🌟 Paramètres principaux de LGBMRegressor
|
||||
1️⃣ objective='regression'
|
||||
|
||||
But : indique le type de problème à résoudre.
|
||||
|
||||
Ici, tu veux prédire une valeur continue (par ex. un rendement futur, un prix, etc.).
|
||||
|
||||
Autres valeurs possibles :
|
||||
|
||||
'binary' → pour classification 0/1
|
||||
|
||||
'multiclass' → pour plusieurs classes
|
||||
|
||||
'regression_l1' → pour des valeurs continues mais avec perte L1 (moins sensible aux outliers)
|
||||
|
||||
📘 En résumé : ici LightGBM cherche à minimiser l’erreur entre la valeur prédite et la valeur réelle.
|
||||
|
||||
2️⃣ metric='rmse'
|
||||
|
||||
But : indique la métrique utilisée pour évaluer la qualité du modèle.
|
||||
|
||||
'rmse' = Root Mean Squared Error (racine de la moyenne des carrés des erreurs)
|
||||
→ pénalise fortement les grosses erreurs.
|
||||
|
||||
'mae' (Mean Absolute Error) est une alternative plus robuste (moins sensible aux outliers).
|
||||
|
||||
Tu peux aussi utiliser plusieurs métriques : metric=['rmse', 'mae'].
|
||||
|
||||
3️⃣ n_estimators=300
|
||||
|
||||
But : nombre d’arbres de décision à construire.
|
||||
|
||||
Chaque arbre apprend à corriger les erreurs du précédent → c’est le boosting.
|
||||
|
||||
Plus ce nombre est grand :
|
||||
|
||||
Meilleure précision potentielle
|
||||
|
||||
Mais risque de surapprentissage et de lenteur
|
||||
|
||||
Typiquement, on le combine avec un petit learning_rate (comme ici 0.05).
|
||||
|
||||
4️⃣ learning_rate=0.05
|
||||
|
||||
But : contrôle l’intensité avec laquelle chaque nouvel arbre corrige les erreurs.
|
||||
|
||||
Si learning_rate ↓, il faut plus d’arbres (n_estimators ↑) pour converger.
|
||||
|
||||
Typiquement :
|
||||
|
||||
0.1 = standard
|
||||
|
||||
0.05 = prudent (meilleur généralisation)
|
||||
|
||||
0.01 = très lent mais précis
|
||||
|
||||
⚖️ Ce paramètre agit comme un “frein” sur l’apprentissage.
|
||||
|
||||
5️⃣ max_depth=7
|
||||
|
||||
But : profondeur maximale des arbres.
|
||||
|
||||
Plus les arbres sont profonds :
|
||||
|
||||
→ plus le modèle capture des relations complexes
|
||||
|
||||
→ mais risque de surapprentissage
|
||||
|
||||
Valeurs typiques :
|
||||
|
||||
3 à 8 pour éviter le surapprentissage
|
||||
|
||||
-1 = pas de limite
|
||||
|
||||
6️⃣ subsample=0.8
|
||||
|
||||
But : fraction de l’échantillon d’entraînement utilisée pour chaque arbre.
|
||||
|
||||
Exemple :
|
||||
|
||||
0.8 = chaque arbre est entraîné sur 80 % des lignes (tirées aléatoirement).
|
||||
|
||||
Permet :
|
||||
|
||||
de réduire le surapprentissage
|
||||
|
||||
d’accélérer l’entraînement
|
||||
|
||||
Si tu veux des résultats très stables → monte à 1.0
|
||||
Si tu veux plus de diversité entre les arbres → garde entre 0.7 et 0.9.
|
||||
|
||||
7️⃣ colsample_bytree=0.8
|
||||
|
||||
But : fraction de colonnes (features) utilisées pour chaque arbre.
|
||||
|
||||
Comme subsample, mais pour les variables.
|
||||
|
||||
Aide à la régularisation : chaque arbre ne voit pas toutes les colonnes → modèle plus robuste.
|
||||
|
||||
Typiquement entre 0.6 et 1.0.
|
||||
|
||||
8️⃣ random_state=42
|
||||
|
||||
But : fixe la graine aléatoire.
|
||||
|
||||
Permet d’obtenir de
|
||||
2813
Zeus_LGBMRegressor.py
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Zeus_LGBMRegressor.py
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Zeus_TensorFlow.py
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Zeus_TensorFlow.py
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Zeus_TensorFlow_1h.py
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Zeus_TensorFlow_1h.py
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Reference in New Issue
Block a user