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Frictrade

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This commit is contained in:
Jérôme Delacotte
2025-12-05 23:27:40 +01:00
parent 3efc95e415
commit ae98d11559
26 changed files with 15887 additions and 38 deletions
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186
GodStra.py
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@@ -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