1201 lines
53 KiB
Python
1201 lines
53 KiB
Python
# 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
|
||
|
||
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
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
from tabulate import tabulate
|
||
|
||
|
||
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
|
||
|
||
class Zeus_8_3_2_B_4_2(IStrategy):
|
||
levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
|
||
startup_candle_count = 24
|
||
|
||
# ROI table:
|
||
minimal_roi = {
|
||
"0": 0.564,
|
||
"567": 0.273,
|
||
"2814": 0.12,
|
||
"7675": 0
|
||
}
|
||
|
||
# Stoploss:
|
||
stoploss = -1 # 0.256
|
||
# Custom stoploss
|
||
use_custom_stoploss = True
|
||
|
||
# Buy hypers
|
||
timeframe = '5m'
|
||
|
||
max_open_trades = 5
|
||
max_amount = 40
|
||
|
||
# DCA config
|
||
position_adjustment_enable = True
|
||
|
||
plot_config = {
|
||
"main_plot": {
|
||
"sma5_1h": {
|
||
"color": "white"
|
||
},
|
||
"sma5_1d": {
|
||
"color": "blue"
|
||
},
|
||
"sma20": {
|
||
"color": "yellow"
|
||
},
|
||
"bb_lowerband": {
|
||
"color": "#da59a6"},
|
||
"bb_upperband": {
|
||
"color": "#da59a6",
|
||
},
|
||
"sma10": {
|
||
"color": "blue"
|
||
},
|
||
"min12_1d": {
|
||
"color": "red"
|
||
},
|
||
"max12_1d": {
|
||
"color": 'red'
|
||
},
|
||
"min50": {
|
||
"color": 'green'
|
||
},
|
||
"max50": {
|
||
"color": 'green'
|
||
}
|
||
},
|
||
"subplots": {
|
||
"Pct": {
|
||
"sma20_pct": {
|
||
'color': "green"
|
||
},
|
||
"down_pct": {
|
||
"color": "blue"
|
||
},
|
||
"down_pct_1h": {
|
||
"color": "red"
|
||
},
|
||
"down_pct_1d": {
|
||
"color": "red"
|
||
}
|
||
},
|
||
"Rsi": {
|
||
"rsi": {
|
||
"color": "pink"
|
||
},
|
||
"rsi_1h": {
|
||
"color": "red"
|
||
},
|
||
"rsi_1d": {
|
||
"color": "blue"
|
||
}
|
||
},
|
||
"Rsi_diff": {
|
||
"rsi_diff_1h": {
|
||
"color": "red"
|
||
},
|
||
"rsi_diff_1d": {
|
||
"color": "blue"
|
||
},
|
||
},
|
||
"Down": {
|
||
"down_count_1h": {
|
||
"color": "green"
|
||
},
|
||
"up_count_1h": {
|
||
"color": "blue"
|
||
}
|
||
},
|
||
# "Diff": {
|
||
# "sma10_diff": {
|
||
# "color": "#74effc"
|
||
# }
|
||
# },
|
||
"smooth": {
|
||
'sma5_diff_sum_1h': {
|
||
"color": "green"
|
||
},
|
||
'sma5_diff2_sum_1h': {
|
||
"color": "blue"
|
||
},
|
||
'mid_smooth_deriv1_1d': {
|
||
"color": "blue"
|
||
},
|
||
'mid_smooth_deriv1_1h': {
|
||
"color": "red"
|
||
},
|
||
'mid_smooth_deriv2_1d': {
|
||
"color": "pink"
|
||
},
|
||
'mid_smooth_deriv2_1h': {
|
||
"color": "#da59a6"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
columns_logged = False
|
||
pairs = {
|
||
pair: {
|
||
"first_buy": 0,
|
||
"last_max": 0,
|
||
"trade_info": {},
|
||
"max_touch": 0.0,
|
||
"last_sell": 0.0,
|
||
"last_buy": 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
|
||
}
|
||
for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC",
|
||
"BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"]
|
||
}
|
||
# 20 20 40 60 100 160 260 420
|
||
# 50 50 100 300 500
|
||
# fibo = [1, 1, 2, 3, 5, 8, 13, 21]
|
||
# my fibo
|
||
# 50 50 50 100 100 150 200 250 350 450 600 1050
|
||
fibo = [1, 1, 1, 2, 2, 3, 4, 5, 7, 9, 12, 16, 21]
|
||
baisse = [1, 2, 3, 5, 7, 10, 14, 19, 26, 35, 47, 63, 84]
|
||
# Ma suite 1 1 1 2 2 3 4 5 7 9 12 16 21
|
||
# Mise 50 50 50 100 100 150 200 250 350 450 600 800 1050
|
||
# Somme Mises 50 100 150 250 350 500 700 950 1300 1750 2350 3150 4200
|
||
# baisse 1 2 3 5 7 10 14 19 26 35 47 63 84
|
||
|
||
trades = list()
|
||
max_profit_pairs = {}
|
||
|
||
protection_percent_buy_lost = IntParameter(1, 10, default=5, space='protection')
|
||
protection_fibo = IntParameter(1, 10, default=2, space='protection')
|
||
sell_allow_decrease = DecimalParameter(0.005, 0.02, default=0.2, decimals=2, space='sell', optimize=True, load=True)
|
||
|
||
data = {
|
||
"B5": [41.0, 41.2, 34.1, 27.5, 35.0, 30.6, 25.2, 29.8, 25.7, 30.6, 14.8],
|
||
"B4": [47.2, 35.8, 39.7, 27.9, 26.5, 19.9, 28.7, 20.8, 29.4, 27.5, 21.6],
|
||
"B3": [48.1, 48.4, 42.8, 32.3, 24.4, 23.6, 28.6, 23.9, 22.7, 25.1, 22.2],
|
||
"B2": [45.6, 46.5, 47.0, 33.2, 34.9, 30.8, 25.8, 30.4, 29.8, 22.6, 35.3],
|
||
"B1": [74.0, 59.9, 63.3, 61.9, 50.0, 41.9, 35.9, 34.4, 37.7, 30.8, 19.3],
|
||
"N0": [65.9, 60.2, 64.5, 67.1, 59.2, 59.2, 44.2, 37.5, 47.1, 34.1, 31.6],
|
||
"H1": [66.5, 75.8, 71.5, 70.8, 69.4, 67.5, 60.1, 52.7, 59.9, 50.9, 38.3],
|
||
"H2": [83.8, 79.4, 80.4, 79.5, 72.8, 70.6, 68.8, 66.1, 68.5, 59.8, 59.6],
|
||
"H3": [77.8, 84.6, 82.0, 81.3, 79.8, 74.0, 67.7, 69.8, 66.5, 57.0, 65.2],
|
||
"H4": [72.1, 83.0, 86.6, 73.6, 77.4, 63.0, 69.6, 67.5, 68.6, 68.6, 56.8],
|
||
"H5": [81.0, 78.5, 76.6, 81.9, 69.5, 75.0, 80.9, 62.9, 66.4, 63.7, 59.6]
|
||
}
|
||
|
||
index_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
|
||
matrix_df = pd.DataFrame(data, index=index_labels)
|
||
|
||
|
||
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((current_time - self.pairs[pair]['last_date']).total_seconds() / 60,0)
|
||
|
||
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()
|
||
# last_candle_12 = dataframe.iloc[-13].squeeze()
|
||
# if (last_candle['close'] < self.pairs[pair]['last_sell'] * 0.99 or minutes > 60 * 5) & (self.pairs[pair]['stop']):
|
||
# print(f"restart {pair} last_sell={self.pairs[pair]['last_sell'] * 0.99} minutes={minutes}")
|
||
# self.pairs[pair]['stop'] = False
|
||
|
||
mid_smooth_label = self.get_mid_smooth_label(last_candle['mid_smooth_deriv1_1h']) # ex. 'B2'
|
||
sma24_diff_label = self.get_sma24_diff_label(last_candle['sma24_diff_1h'])
|
||
|
||
val = self.approx_val_from_bins(row_label=sma24_diff_label, col_label=mid_smooth_label)
|
||
|
||
# allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
|
||
allow_to_buy = not self.pairs[pair]['stop'] and val > 50 #not last_candle['tendency'] in ('B-', 'B--') # (rate <= float(limit)) | (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
|
||
|
||
dispo = round(self.wallets.get_available_stake_amount())
|
||
self.printLineLog()
|
||
|
||
stake_amount = self.adjust_stake_amount(pair, last_candle)
|
||
|
||
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()
|
||
|
||
allow_to_sell = (last_candle['percent'] < 0)
|
||
|
||
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.pairs[pair]['max_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(trade.calc_profit(rate, amount), 2)
|
||
)
|
||
self.pairs[pair]['max_touch'] = 0
|
||
self.pairs[pair]['last_buy'] = 0
|
||
self.pairs[pair]['last_date'] = current_time
|
||
|
||
return (allow_to_sell) | (exit_reason == 'force_exit')
|
||
|
||
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
|
||
proposed_stake: float, min_stake: float, max_stake: float,
|
||
**kwargs) -> float:
|
||
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
|
||
current_candle = dataframe.iloc[-1].squeeze()
|
||
adjusted_stake_amount = self.adjust_stake_amount(pair, current_candle)
|
||
|
||
# print(f"{pair} adjusted_stake_amount{adjusted_stake_amount}")
|
||
|
||
# Use default stake amount.
|
||
return adjusted_stake_amount
|
||
|
||
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()
|
||
|
||
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['haclose'], self.pairs[pair]['last_max'])
|
||
|
||
count_of_buys = trade.nr_of_successful_entries
|
||
|
||
self.pairs[pair]['count_of_buys'] = count_of_buys
|
||
self.pairs[pair]['current_profit'] = current_profit
|
||
self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], current_profit)
|
||
|
||
if (last_candle['mid_smooth_deriv1'] >= 0):
|
||
return None
|
||
if (last_candle['tendency'] in ('H++', 'H+')) and (last_candle['rsi'] < 80):
|
||
return None
|
||
|
||
baisse = self.pairs[pair]['max_profit'] - current_profit
|
||
mx = self.pairs[pair]['max_profit'] / 5
|
||
if (baisse > mx) & (current_profit > expected_profit): #last_candle['min_max200'] / 3):
|
||
self.trades = list()
|
||
return 'mx_' + str(count_of_buys)
|
||
if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit):
|
||
self.trades = list()
|
||
return 'pft_' + str(count_of_buys)
|
||
|
||
self.pairs[pair]['max_touch'] = max(last_candle['haclose'], self.pairs[pair]['max_touch'])
|
||
|
||
def informative_pairs(self):
|
||
# get access to all pairs available in whitelist.
|
||
pairs = self.dp.current_whitelist()
|
||
informative_pairs = [(pair, '1d') for pair in pairs]
|
||
informative_pairs += [(pair, '1h') for pair in pairs]
|
||
|
||
return informative_pairs
|
||
|
||
from typing import List
|
||
|
||
def multi_step_interpolate(self, pct: float, thresholds: List[float], factors: List[float]) -> float:
|
||
if pct <= thresholds[0]:
|
||
return factors[0]
|
||
if pct >= thresholds[-1]:
|
||
return factors[-1]
|
||
|
||
for i in range(1, len(thresholds)):
|
||
if pct <= thresholds[i]:
|
||
# interpolation linéaire entre thresholds[i-1] et thresholds[i]
|
||
return factors[i - 1] + (pct - thresholds[i - 1]) * (factors[i] - factors[i - 1]) / (
|
||
thresholds[i] - thresholds[i - 1])
|
||
|
||
# Juste au cas où (devrait jamais arriver)
|
||
return factors[-1]
|
||
|
||
def interpolate_factor(self, pct: float, start_pct: float = 5, end_pct: float = 30,
|
||
start_factor: float = 1.0, end_factor: float = 2.0) -> float:
|
||
if pct <= start_pct:
|
||
return start_factor
|
||
if pct >= end_pct:
|
||
return end_factor
|
||
# interpolation linéaire
|
||
return start_factor + (pct - start_pct) * (end_factor - start_factor) / (end_pct - start_pct)
|
||
|
||
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':
|
||
return
|
||
if self.columns_logged % 30 == 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}|{'Buys':>4}| {'Stake':>5} |"
|
||
f"sum_1h|sum_1d|Tdc|Tdh|Tdd| drv1 |drv_1h|drv_1d|"
|
||
)
|
||
self.printLineLog()
|
||
|
||
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 = ''
|
||
# if last_candle is not None:
|
||
# if (not np.isnan(last_candle['rsi_1d'])) and (not np.isnan(last_candle['rsi_1h'])):
|
||
# rsi = str(int(last_candle['rsi_1d'])) + " " + str(int(last_candle['rsi_1h']))
|
||
# if (not np.isnan(last_candle['rsi_pct_1d'])) and (not np.isnan(last_candle['rsi_pct_1h'])):
|
||
# rsi_pct = str(int(10000 * last_candle['bb_mid_pct_1d'])) + " " + str(
|
||
# int(last_candle['rsi_pct_1d'])) + " " + str(int(last_candle['rsi_pct_1h']))
|
||
|
||
# first_rate = self.percent_threshold.value
|
||
# last_rate = self.threshold.value
|
||
# action = self.color_line(action, action)
|
||
sma5_1d = ''
|
||
sma5_1h = ''
|
||
|
||
sma5 = str(sma5_1d) + ' ' + str(sma5_1h)
|
||
|
||
last_lost = round((last_candle['haclose'] - self.pairs[pair]['max_touch']) / self.pairs[pair]['max_touch'], 3)
|
||
|
||
max_touch = '' #round(last_candle['max12_1d'], 1) #round(self.pairs[pair]['max_touch'], 1)
|
||
pct_max = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3) # round(100 * self.pairs[pair]['current_profit'], 1)
|
||
|
||
if trade_type is not None:
|
||
if np.isnan(last_candle['rsi_1d']):
|
||
string = ' '
|
||
else:
|
||
string = (str(int(last_candle['rsi_1d']))) + " " + str(int(last_candle['rsi_diff_1d']))
|
||
trade_type = trade_type \
|
||
+ " " + string \
|
||
+ " " + str(int(last_candle['rsi_1h'])) \
|
||
+ " " + str(int(last_candle['rsi_diff_1h']))
|
||
|
||
self.printLog(
|
||
f"| {date:<16} | {action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} |{rate or '-':>9}| {dispo or '-':>6} "
|
||
f"| {profit or '-':>8} | {pct_max or '-':>6} | {round(self.pairs[pair]['max_touch'], 2) or '-':>11} | {last_lost or '-':>12} "
|
||
f"| {round(self.pairs[pair]['last_max'], 0) or '-':>7} |{buys or '-':>4}|{stake or '-':>7}"
|
||
f"|{round(last_candle['sma5_diff_sum_1h'], 2) or '-':>6}|{round(last_candle['sma5_diff_sum_1d'], 2) or '-':>6}"
|
||
f"|{last_candle['tendency'] or '-':>3}|{last_candle['tendency_1h'] or '-':>3}|{last_candle['tendency_1d'] or '-':>3}"
|
||
f"|{round(last_candle['mid_smooth_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1h'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1d'],3) or '-' :>6}|"
|
||
# f"|{round(last_candle['mid_smooth_deriv2']) or '-' :>3 }|{round(last_candle['mid_smooth_deriv2_1h']) or '-':>5}|{round(last_candle['mid_smooth_deriv2_1d']) or '-':>5}"
|
||
)
|
||
|
||
def printLineLog(self):
|
||
# f"sum1h|sum1d|Tdc|Tdh|Tdd| drv1 |drv_1h|drv_1d|"
|
||
self.printLog(
|
||
f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 10}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}+{'-' * 4}+{'-' * 7}+"
|
||
f"{'-' * 6}+{'-' * 6}+{'-' * 3}+{'-' * 3}+{'-' * 3}+{'-' * 6}+{'-' * 6}+{'-' * 6}+"
|
||
)
|
||
|
||
def printLog(self, str):
|
||
if not self.dp.runmode.value in ('backtest', 'hyperopt'):
|
||
logger.info(str)
|
||
else:
|
||
print(str)
|
||
|
||
def add_tendency_column(self, dataframe: pd.DataFrame) -> pd.DataFrame:
|
||
def tag_by_derivatives(row):
|
||
d1 = row['mid_smooth_deriv1']
|
||
d2 = row['mid_smooth_deriv2']
|
||
d1_lim_inf = -0.01
|
||
d1_lim_sup = 0.01
|
||
if d1 >= d1_lim_inf and d1 <= d1_lim_sup: # and d2 >= d2_lim_inf and d2 <= d2_lim_sup:
|
||
return 'P' # Palier
|
||
if d1 == 0.0:
|
||
return 'DH' if d2 > 0 else 'DB' #Depart Hausse / Départ Baisse
|
||
if d1 > d1_lim_sup:
|
||
return 'H++' if d2 > 0 else 'H+' #Acceleration Hausse / Ralentissement Hausse
|
||
if d1 < d1_lim_inf:
|
||
return 'B--' if d2 < 0 else 'B-' # Accéleration Baisse / Ralentissement Baisse
|
||
return 'Mid'
|
||
|
||
dataframe['tendency'] = dataframe.apply(tag_by_derivatives, axis=1)
|
||
return dataframe
|
||
|
||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
# Add all ta features
|
||
pair = metadata['pair']
|
||
|
||
heikinashi = qtpylib.heikinashi(dataframe)
|
||
dataframe['haopen'] = heikinashi['open']
|
||
dataframe['haclose'] = heikinashi['close']
|
||
dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
|
||
|
||
dataframe['pct_change'] = dataframe['close'].pct_change(5)
|
||
dataframe = self.calculateTendency(dataframe)
|
||
|
||
dataframe['min'] = talib.MIN(dataframe['close'], timeperiod=200)
|
||
dataframe['min12'] = talib.MIN(dataframe['close'], timeperiod=12)
|
||
|
||
dataframe['min50'] = talib.MIN(dataframe['close'], timeperiod=50)
|
||
dataframe['min200'] = talib.MIN(dataframe['close'], timeperiod=200)
|
||
dataframe['max200'] = talib.MAX(dataframe['close'], timeperiod=200)
|
||
|
||
dataframe['max50'] = talib.MAX(dataframe['close'], timeperiod=50)
|
||
dataframe['max144'] = talib.MAX(dataframe['close'], timeperiod=144)
|
||
dataframe['min_max50'] = (dataframe['max50'] - dataframe['min50']) / dataframe['min50']
|
||
|
||
dataframe['min_max200'] = (dataframe['max200'] - dataframe['min200']) / dataframe['min200']
|
||
dataframe['max200_diff'] = (dataframe['max200'] - dataframe['close']) / dataframe['close']
|
||
dataframe['max50_diff'] = (dataframe['max50'] - dataframe['close']) / dataframe['close']
|
||
|
||
dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5)
|
||
dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10)
|
||
dataframe['sma10_diff'] = 100 * dataframe['sma10'].diff() / dataframe['sma10']
|
||
dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20)
|
||
dataframe['sma20_pct'] = 100 * dataframe['sma20'].diff() / dataframe['sma20']
|
||
|
||
dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
|
||
dataframe["percent3"] = (dataframe["close"] - dataframe["open"].shift(3)) / dataframe["open"].shift(3)
|
||
dataframe["percent5"] = (dataframe["close"] - dataframe["open"].shift(5)) / dataframe["open"].shift(5)
|
||
dataframe["percent12"] = (dataframe["close"] - dataframe["open"].shift(12)) / dataframe["open"].shift(12)
|
||
dataframe["percent24"] = (dataframe["close"] - dataframe["open"].shift(24)) / dataframe["open"].shift(24)
|
||
dataframe["percent48"] = (dataframe["close"] - dataframe["open"].shift(48)) / dataframe["open"].shift(48)
|
||
dataframe["percent_max_144"] = (dataframe["close"] - dataframe["max144"]) / dataframe["close"]
|
||
|
||
# print(metadata['pair'])
|
||
dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
|
||
dataframe['rsi_diff'] = dataframe['rsi'].diff()
|
||
dataframe['rsi_diff_2'] = dataframe['rsi_diff'].diff()
|
||
|
||
# Bollinger Bands
|
||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
||
dataframe['bb_lowerband'] = bollinger['lower']
|
||
dataframe['bb_middleband'] = bollinger['mid']
|
||
dataframe['bb_upperband'] = bollinger['upper']
|
||
dataframe["bb_percent"] = (
|
||
(dataframe["close"] - dataframe["bb_lowerband"]) /
|
||
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
|
||
)
|
||
dataframe["bb_width"] = (
|
||
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_upperband"]
|
||
)
|
||
# Normalization
|
||
|
||
dataframe['average_line'] = dataframe['close'].mean()
|
||
dataframe['average_line_50'] = talib.MIDPOINT(dataframe['close'], timeperiod=50)
|
||
|
||
dataframe['average_line_288'] = talib.MIDPOINT(dataframe['close'], timeperiod=288)
|
||
dataframe['average_line_288_098'] = dataframe['average_line_288'] * 0.98
|
||
dataframe['average_line_288_099'] = dataframe['average_line_288'] * 0.99
|
||
|
||
# Compter les baisses consécutives
|
||
self.calculateDownAndUp(dataframe, limit=0.0001)
|
||
|
||
# dataframe = self.apply_regression_derivatives(dataframe, column='mid', window=24, degree=3)
|
||
|
||
################### INFORMATIVE 1h
|
||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
|
||
heikinashi = qtpylib.heikinashi(informative)
|
||
informative['haopen'] = heikinashi['open']
|
||
informative['haclose'] = heikinashi['close']
|
||
informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
|
||
|
||
informative = self.calculateTendency(informative, 12)
|
||
# informative = self.apply_regression_derivatives(informative, column='mid', window=5, degree=3)
|
||
# informative['volatility'] = talib.STDDEV(informative['close'], timeperiod=14) / informative['close']
|
||
# informative['atr'] = (talib.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)) / informative['close']
|
||
informative['rsi'] = talib.RSI(informative['close']) #, timeperiod=7)
|
||
informative['rsi_diff'] = informative['rsi'].diff()
|
||
informative['rsi_sum'] = (informative['rsi'].rolling(7).sum() - 350) / 7
|
||
informative['rsi_sum_diff'] = informative['rsi_sum'].diff()
|
||
informative['rsi_diff_2'] = informative['rsi_diff'].diff()
|
||
informative['max12'] = talib.MAX(informative['close'], timeperiod=12)
|
||
informative['min12'] = talib.MIN(informative['close'], timeperiod=12)
|
||
|
||
informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
||
informative['sma5_diff'] = 100 * informative['sma5'].diff() / informative['sma5']
|
||
informative['sma24'] = talib.SMA(informative, timeperiod=24)
|
||
informative['sma24_diff'] = 100 * informative['sma24'].diff() / informative['sma24']
|
||
informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5']
|
||
informative['sma5_diff_sum'] = (informative['sma5_pct'].rolling(5).sum()) / 5
|
||
informative['sma5_diff2_sum'] = informative['sma5_diff_sum'].diff()
|
||
|
||
self.calculateDownAndUp(informative, limit=0.0012)
|
||
|
||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
|
||
|
||
################### INFORMATIVE 1d
|
||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
|
||
informative = self.calculateTendency(informative, 7)
|
||
# informative['volatility'] = talib.STDDEV(informative['close'], timeperiod=14) / informative['close']
|
||
# informative['atr'] = (talib.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)) / informative['close']
|
||
|
||
# informative = self.apply_regression_derivatives(informative, column='mid', window=5, degree=3)
|
||
informative['max12'] = talib.MAX(informative['close'], timeperiod=12)
|
||
informative['min12'] = talib.MIN(informative['close'], timeperiod=12)
|
||
informative['max3'] = talib.MAX(informative['close'], timeperiod=3)
|
||
informative['min3'] = talib.MIN(informative['close'], timeperiod=3)
|
||
|
||
informative['rsi'] = talib.RSI(informative['close']) #, timeperiod=7)
|
||
informative['rsi_diff'] = informative['rsi'].diff()
|
||
informative['rsi_sum'] = (informative['rsi'].rolling(7).sum() - 350) / 7
|
||
informative['rsi_diff_2'] = informative['rsi_diff'].diff()
|
||
|
||
informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
||
informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5']
|
||
informative['sma5_diff_sum'] = (informative['sma5_pct'].rolling(5).sum()) / 5
|
||
informative['sma5_diff2_sum'] = informative['sma5_diff_sum'].diff()
|
||
|
||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
|
||
|
||
dataframe['last_price'] = dataframe['close']
|
||
dataframe['first_price'] = dataframe['close']
|
||
# dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
|
||
# dataframe['close01'] = dataframe.iloc[-1]['close'] * 1.01
|
||
# dataframe['limit'] = dataframe['close']
|
||
count_buys = 0
|
||
if self.dp:
|
||
if self.dp.runmode.value in ('live', 'dry_run'):
|
||
self.getOpenTrades()
|
||
|
||
for trade in self.trades:
|
||
if trade.pair != pair:
|
||
continue
|
||
print(trade)
|
||
filled_buys = trade.select_filled_orders('buy')
|
||
count = 0
|
||
amount = 0
|
||
for buy in filled_buys:
|
||
if count == 0:
|
||
dataframe['first_price'] = buy.price
|
||
# 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
|
||
print(buy)
|
||
count = count + 1
|
||
amount += buy.price * buy.filled
|
||
# dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
|
||
count_buys = count
|
||
# dataframe['limit'] = dataframe['last_price'] * (1 - self.baisse[count] / 100)
|
||
# dataframe['amount'] = amount
|
||
print(f"amount= {amount}")
|
||
|
||
dataframe['futur_percent_1h'] = 100 * (dataframe['close'].shift(-12) - dataframe['close']) / dataframe['close']
|
||
dataframe['futur_percent_3h'] = 100 * (dataframe['close'].shift(-36) - dataframe['close']) / dataframe['close']
|
||
dataframe['futur_percent_5h'] = 100 * (dataframe['close'].shift(-60) - dataframe['close']) / dataframe['close']
|
||
dataframe['futur_percent_12h'] = 100 * (dataframe['close'].shift(-144) - dataframe['close']) / dataframe['close']
|
||
|
||
return dataframe
|
||
|
||
def calculateDownAndUp(self, dataframe, limit=0.0001):
|
||
dataframe['down'] = dataframe['hapercent'] <= limit
|
||
dataframe['up'] = dataframe['hapercent'] >= limit
|
||
dataframe['down_count'] = - dataframe['down'].astype(int) * (
|
||
dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
|
||
dataframe['up_count'] = dataframe['up'].astype(int) * (
|
||
dataframe['up'].groupby((dataframe['up'] != dataframe['up'].shift()).cumsum()).cumcount() + 1)
|
||
# Créer une colonne vide
|
||
dataframe['down_pct'] = self.calculateUpDownPct(dataframe, 'down_count')
|
||
dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count')
|
||
|
||
def calculateTendency(self, dataframe, window=12):
|
||
dataframe['mid'] = dataframe['open'] + (dataframe['close'] - dataframe['open']) / 2
|
||
# 2. Calcul du lissage par moyenne mobile médiane
|
||
dataframe['mid_smooth'] = dataframe['close'].rolling(window=window, center=True, min_periods=1).median().rolling(
|
||
3).mean()
|
||
# 2. Dérivée première = différence entre deux bougies successives
|
||
dataframe['mid_smooth_deriv1'] = round(100 * dataframe['mid_smooth'].diff() / dataframe['mid_smooth'], 4)
|
||
# 3. Dérivée seconde = différence de la dérivée première
|
||
dataframe['mid_smooth_deriv2'] = round(10 * dataframe['mid_smooth_deriv1'].diff(), 4)
|
||
dataframe = self.add_tendency_column(dataframe)
|
||
return dataframe
|
||
|
||
def getOpenTrades(self):
|
||
# if len(self.trades) == 0:
|
||
print('search open trades')
|
||
self.trades = Trade.get_open_trades()
|
||
return self.trades
|
||
|
||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
pair = metadata['pair']
|
||
|
||
# self.getOpenTrades()
|
||
expected_profit = self.expectedProfit(pair, dataframe.iloc[-1])
|
||
# self.getBinanceOrderBook(pair, dataframe)
|
||
last_candle = dataframe.iloc[-1].squeeze()
|
||
|
||
print("---------------" + pair + "----------------")
|
||
print('adjust stake amount ' + str(self.adjust_stake_amount(pair, dataframe.iloc[-1])))
|
||
# print('adjust exit price ' + str(self.adjust_exit_price(dataframe.iloc[-1])))
|
||
print('calcul expected_profit ' + str(expected_profit))
|
||
|
||
buy_level = dataframe['average_line_50'] #dataframe['buy_level'] # self.get_buy_level(pair, dataframe)
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe['max200_diff'] >= 0.01)
|
||
& (dataframe['percent12'] < -0.002)
|
||
# & (dataframe['pct_change'] < 0)
|
||
& (dataframe['open'] < dataframe['average_line_288_099'])
|
||
& (dataframe['open'] < dataframe['average_line_50'])
|
||
# & (dataframe['percent'] >= -0.0005)
|
||
& (dataframe['min12'].shift(2) == dataframe['min12'])
|
||
& (dataframe['up_count'] > 0)
|
||
& (dataframe["bb_width"] > 0.01)
|
||
), ['enter_long', 'enter_tag']] = (1, 'mx200')
|
||
|
||
dataframe.loc[
|
||
(
|
||
# (dataframe['down_count'].shift(1) < - 1)
|
||
# & (dataframe['down_count'] == 0)
|
||
(dataframe['mid_smooth_deriv1'] > 0)
|
||
), ['enter_long', 'enter_tag']] = (1, 'down')
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe['low'] < dataframe['min200'])
|
||
& (dataframe['min50'] == dataframe['min50'].shift(3))
|
||
& (dataframe['tendency'] != "B-")
|
||
), ['enter_long', 'enter_tag']] = (1, 'low')
|
||
|
||
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
|
||
|
||
if self.dp.runmode.value in ('backtest'):
|
||
dataframe.to_feather(f"user_data/data/binance/{metadata['pair'].replace('/', '_')}_df.feather")
|
||
|
||
df = dataframe
|
||
|
||
# # Définition des tranches pour les dérivées
|
||
# bins_deriv = [-np.inf, -0.05, -0.01, 0.01, 0.05, np.inf]
|
||
# labels = ['forte baisse', 'légère baisse', 'neutre', 'légère hausse', 'forte hausse']
|
||
#
|
||
# # Ajout des colonnes bin (catégorisation)
|
||
# df[f"{indic_1}_bin"] = pd.cut(df['mid_smooth_deriv1_1h'], bins=bins_deriv, labels=labels)
|
||
# df[f"{indic_2}_bin"] = pd.cut(df['mid_smooth_deriv1_1d'], bins=bins_deriv, labels=labels)
|
||
#
|
||
# # Colonnes de prix futur à analyser
|
||
# futur_cols = ['futur_percent_1h', 'futur_percent_2h', 'futur_percent_3h', 'futur_percent_4h', 'futur_percent_5h']
|
||
#
|
||
# # Calcul des moyennes et des effectifs
|
||
# grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"])[futur_cols].agg(['mean', 'count'])
|
||
#
|
||
# pd.set_option('display.width', 200) # largeur max affichage
|
||
# pd.set_option('display.max_columns', None)
|
||
|
||
# Colonnes à traiter
|
||
futur_cols = ['futur_percent_1h', 'futur_percent_3h', 'futur_percent_5h', 'futur_percent_12h']
|
||
|
||
# Tranches équitables par quantiles
|
||
# Exemple pour 10 quantiles
|
||
labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
|
||
|
||
indic_1 = 'mid_smooth_deriv1_1h'
|
||
indic_2 = 'sma24_diff_1h'
|
||
|
||
df[f"{indic_1}_bin"], bins_1h = pd.qcut(df[f"{indic_1}"], q=11, labels=labels, retbins=True, duplicates='drop')
|
||
df[f"{indic_2}_bin"], bins_1d = pd.qcut(df[f"{indic_2}"], q=11, labels=labels, retbins=True, duplicates='drop')
|
||
|
||
pd.set_option('display.max_columns', None)
|
||
pd.set_option('display.width', 300) # largeur max affichage
|
||
|
||
# Afficher les bornes
|
||
print(f"Bornes des quantiles pour {indic_1} :", bins_1h)
|
||
print(f"Bornes des quantiles pour {indic_2} :", bins_1d)
|
||
|
||
# Agrégation
|
||
grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[futur_cols].agg(['mean', 'count'])
|
||
|
||
# Affichage
|
||
|
||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||
print(grouped.round(4))
|
||
|
||
# Ajout des probabilités de hausse
|
||
for col in futur_cols:
|
||
df[f"{col}_is_up"] = df[col] > 0
|
||
|
||
# Calcul de la proba de hausse
|
||
proba_up = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[f"{col}_is_up"].mean().unstack()
|
||
|
||
print(f"\nProbabilité de hausse pour {col} (en %):")
|
||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||
print((proba_up * 100).round(1))
|
||
|
||
return dataframe
|
||
|
||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['mid_smooth_deriv1'] == 0)
|
||
# & (dataframe['mid_smooth_deriv1'].shift(1) > 0)
|
||
# ), ['sell', 'exit_long']] = (1, 'sell_sma5_pct_1h')
|
||
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
|
||
if trade.has_open_orders:
|
||
print("skip open orders")
|
||
return None
|
||
if (self.wallets.get_available_stake_amount() < 50): # or trade.stake_amount >= max_stake:
|
||
return 0
|
||
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
||
last_candle = dataframe.iloc[-1].squeeze()
|
||
last_candle_1 = dataframe.iloc[-2].squeeze()
|
||
last_candle_2 = dataframe.iloc[-3].squeeze()
|
||
last_candle_3 = dataframe.iloc[-4].squeeze()
|
||
last_candle_previous_1h = dataframe.iloc[-13].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())
|
||
days_since_first_buy = (current_time - trade.open_date_utc).days
|
||
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
||
|
||
if (len(dataframe) < 1):
|
||
print("skip dataframe")
|
||
return None
|
||
pair = trade.pair
|
||
if self.dp.runmode.value in ('dry_run'):
|
||
if pair not in ('BTC/USDT', 'BTC/USDC', 'XRP/USDT', 'XRP/USDC', 'ETH/USDT', 'ETH/USDC'):
|
||
print(f"skip pair {pair}")
|
||
return None
|
||
else:
|
||
if pair not in ('BTC/USDT', 'BTC/USDC'):
|
||
print(f"skip pair {pair}")
|
||
return None
|
||
|
||
count_of_buys = trade.nr_of_successful_entries
|
||
|
||
# if (days_since_first_buy >= 5 and count_of_buys >= 4 and last_candle['sma5_pct_1d'] < 0):
|
||
# # print(f"waiting day increase pair {pair}")
|
||
# return None
|
||
|
||
# if 'buy' in last_candle:
|
||
# condition = (last_candle['buy'] == 1)
|
||
# else:
|
||
# condition = False
|
||
# self.protection_nb_buy_lost.value
|
||
|
||
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
|
||
pct_first = 0
|
||
if self.pairs[pair]['first_buy']:
|
||
pct_first = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
|
||
|
||
pct = 0.012
|
||
if count_of_buys == 1:
|
||
pct_max = current_profit
|
||
else:
|
||
if self.pairs[trade.pair]['last_buy']:
|
||
pct_max = round((last_candle['close'] - self.pairs[trade.pair]['last_buy']) / self.pairs[trade.pair]['last_buy'], 4)
|
||
else:
|
||
pct_max = - pct
|
||
|
||
lim = - pct - (count_of_buys * 0.001)
|
||
# print(f"{trade.pair} current_profit={current_profit} count_of_buys={count_of_buys} pct_max={pct_max:.3f} lim={lim:.3f} rsi_diff_1f={last_candle['rsi_diff_1h']}")
|
||
|
||
mid_smooth_label = self.get_mid_smooth_label(last_candle['mid_smooth_deriv1_1h']) # ex. 'B2'
|
||
sma24_diff_label = self.get_sma24_diff_label(last_candle['sma24_diff_1h'])
|
||
|
||
val = self.approx_val_from_bins(row_label=sma24_diff_label, col_label=mid_smooth_label)
|
||
# print(f"Valeur approximée pour B3 / H2 : {val:.2f}")
|
||
|
||
# if (days_since_open > count_of_buys) & (0 < count_of_buys <= max_buys) & (current_rate <= limit) & (last_candle['enter_long'] == 1):
|
||
limit_buy = 20
|
||
if (count_of_buys < limit_buy) \
|
||
and (last_candle['enter_long'] == 1) \
|
||
and (pct_max < lim and val > 50 and last_candle['mid_smooth_deriv1_1d'] > - 1):
|
||
try:
|
||
|
||
max_amount = self.config.get('stake_amount', 100) * 2.5
|
||
stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
|
||
self.adjust_stake_amount(pair, last_candle) - 10 * pct_first / pct) # min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys])
|
||
|
||
trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
|
||
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, 4), # round(current_profit * trade.stake_amount, 2),
|
||
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
|
||
except Exception as exception:
|
||
print(exception)
|
||
return None
|
||
|
||
# if (count_of_buys < limit_buy and pct_max > pct and current_profit > 0.004) \
|
||
# and (last_candle['rsi_diff_1h'] >= -5) \
|
||
# and (last_candle['tendency'] in ('P', 'H++', 'DH', 'H+')) \
|
||
# and (last_candle['mid_smooth_deriv1'] > 0.015):
|
||
# try:
|
||
# trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
|
||
# self.log_trade(
|
||
# last_candle=last_candle,
|
||
# date=current_time,
|
||
# action="Gain +",
|
||
# dispo=dispo,
|
||
# pair=trade.pair,
|
||
# rate=current_rate,
|
||
# trade_type=trade_type,
|
||
# profit=round(current_profit, 4), # round(current_profit * trade.stake_amount, 2),
|
||
# 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
|
||
# except Exception as exception:
|
||
# print(exception)
|
||
# return None
|
||
|
||
return None
|
||
|
||
def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
|
||
# Calculer le minimum des 14 derniers jours
|
||
base_stake_amount = self.config.get('stake_amount', 100) # Montant de base configuré
|
||
|
||
first_price = self.pairs[pair]['first_buy']
|
||
if (first_price == 0):
|
||
first_price = last_candle['close']
|
||
|
||
last_max = last_candle['max12_1d']
|
||
pct = 5
|
||
if last_max > 0:
|
||
pct = 100 * (last_max - first_price) / last_max
|
||
thresholds = [2, 5, 10, 20]
|
||
factors = [1, 1.25, 1.5, 2.0]
|
||
|
||
factor = self.multi_step_interpolate(pct, thresholds, factors)
|
||
|
||
adjusted_stake_amount = base_stake_amount * factor #max(base_stake_amount, min(100, base_stake_amount * percent_4))
|
||
|
||
return adjusted_stake_amount
|
||
|
||
def expectedProfit(self, pair: str, last_candle: DataFrame):
|
||
|
||
expected_profit = 0.004 #min(0.01, first_max)
|
||
|
||
# print(
|
||
# f"Expected profit price={current_price:.4f} min_max={min_max:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} percent={percent:.4f} expected_profit={expected_profit:.4f}")
|
||
return expected_profit
|
||
|
||
def calculateUpDownPct(self, dataframe, key):
|
||
down_pct_values = np.full(len(dataframe), np.nan)
|
||
# Remplir la colonne avec les bons calculs
|
||
for i in range(len(dataframe)):
|
||
shift_value = abs(int(dataframe[key].iloc[i])) # Récupérer le shift actuel
|
||
if i - shift_value > 1: # Vérifier que le shift ne dépasse pas l'index
|
||
down_pct_values[i] = 100 * (dataframe['close'].iloc[i] - dataframe['close'].iloc[i - shift_value]) / \
|
||
dataframe['close'].iloc[i - shift_value]
|
||
return down_pct_values
|
||
|
||
# ✅ Première dérivée(variation ou pente)
|
||
# Positive: la courbe est croissante → tendance haussière.
|
||
# Négative: la courbe est décroissante → tendance baissière.
|
||
# Proche de 0: la courbe est plate → marché stable ou en transition.
|
||
#
|
||
# Applications:
|
||
# Détecter les points d’inflexion(changement de tendance) quand elle s’annule.\
|
||
# Analyser la vitesse d’un mouvement(plus elle est forte, plus le mouvement est impulsif).
|
||
#
|
||
# ✅ Seconde dérivée(accélération ou concavité)
|
||
# Positive: la pente augmente → accélération de la hausse ou ralentissement de la baisse.
|
||
# Négative: la pente diminue → accélération de la baisse ou ralentissement de la hausse.
|
||
# Changement de signe: indique souvent un changement de courbure, utile pour prévoir des retournements.
|
||
#
|
||
# Exemples:
|
||
# 🟢 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.
|
||
#
|
||
# Filtrer les signaux: ne prendre un signal haussier que si dérivée1 > 0 et dérivée2 > 0.
|
||
# Détecter les zones de retournement: quand dérivée1 ≈ 0 et que dérivée2 change de signe.
|
||
def apply_regression_derivatives(self,
|
||
dataframe: DataFrame,
|
||
column: str = 'close',
|
||
window: int = 50,
|
||
degree: int = 3,
|
||
future_offset: int = 10 # projection à n bougies après
|
||
) -> DataFrame:
|
||
df = dataframe.copy()
|
||
|
||
regression_fit = []
|
||
deriv1 = []
|
||
deriv2 = []
|
||
regression_future_fit = []
|
||
regression_future_deriv1 = []
|
||
regression_future_deriv2 = []
|
||
|
||
for i in range(len(df)):
|
||
if i < window or i + future_offset >= len(df):
|
||
regression_fit.append(np.nan)
|
||
deriv1.append(np.nan)
|
||
deriv2.append(np.nan)
|
||
regression_future_fit.append(np.nan)
|
||
regression_future_deriv1.append(np.nan)
|
||
regression_future_deriv2.append(np.nan)
|
||
continue
|
||
|
||
y = df[column].iloc[i - window:i].values
|
||
x = np.arange(window)
|
||
|
||
coeffs = np.polyfit(x, y, degree)
|
||
poly = np.poly1d(coeffs)
|
||
|
||
x_now = window - 1
|
||
x_future = x_now + future_offset
|
||
|
||
regression_fit.append(poly(x_now))
|
||
deriv1.append(np.polyder(poly, 1)(x_now))
|
||
deriv2.append(np.polyder(poly, 2)(x_now))
|
||
|
||
regression_future_fit.append(poly(x_future))
|
||
regression_future_deriv1.append(np.polyder(poly, 1)(x_future))
|
||
regression_future_deriv2.append(np.polyder(poly, 2)(x_future))
|
||
|
||
df['regression_fit'] = regression_fit
|
||
df['regression_deriv1'] = deriv1
|
||
df['regression_deriv2'] = deriv2
|
||
|
||
df['regression_future_fit'] = regression_future_fit
|
||
df['regression_future_deriv1'] = regression_future_deriv1
|
||
df['regression_future_deriv2'] = regression_future_deriv2
|
||
|
||
return df
|
||
|
||
def get_mid_smooth_label(self, value):
|
||
bins = [-2.0622, -0.1618, -0.0717, -0.0353, -0.0135, 0.0, 0.0085, 0.0276, 0.0521, 0.0923, 0.1742, 2.3286]
|
||
labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
|
||
for i in range(len(bins) - 1):
|
||
if bins[i] <= value < bins[i + 1]:
|
||
return labels[i]
|
||
return labels[-1] # cas limite pour la borne max
|
||
|
||
def get_sma24_diff_label(self, value):
|
||
bins = [-0.84253877, -0.13177195, -0.07485074, -0.04293497, -0.02033502, -0.00215711,
|
||
0.01411933, 0.03308264, 0.05661652, 0.09362708, 0.14898214, 0.50579505]
|
||
labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
|
||
for i in range(len(bins) - 1):
|
||
if bins[i] <= value < bins[i + 1]:
|
||
return labels[i]
|
||
return labels[-1]
|
||
|
||
import numpy as np
|
||
import pandas as pd
|
||
|
||
def interpolated_val_from_bins(self, row_pos, col_pos):
|
||
"""
|
||
Renvoie une approximation interpolée (bilinéaire) d'une valeur dans la matrice
|
||
à partir de positions flottantes dans l'index (ligne) et les colonnes.
|
||
|
||
Parameters:
|
||
matrix_df (pd.DataFrame): Matrice des probabilités (index/colonnes = labels).
|
||
row_pos (float): Position réelle de la ligne (0 = B5, 10 = H5).
|
||
col_pos (float): Position réelle de la colonne (0 = B5, 10 = H5).
|
||
|
||
Returns:
|
||
float: Valeur interpolée, ou NaN si en dehors des bornes.
|
||
"""
|
||
|
||
# Labels ordonnés
|
||
labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
|
||
n = len(labels)
|
||
|
||
# Vérification des limites
|
||
if not (0 <= row_pos <= n - 1) or not (0 <= col_pos <= n - 1):
|
||
return np.nan
|
||
|
||
# Conversion des labels -> matrice
|
||
matrix = self.matrix_df.reindex(index=labels, columns=labels).values
|
||
|
||
# Coordonnées entières (inférieures)
|
||
i = int(np.floor(row_pos))
|
||
j = int(np.floor(col_pos))
|
||
|
||
# Coefficients pour interpolation
|
||
dx = row_pos - i
|
||
dy = col_pos - j
|
||
|
||
# Précautions sur les bords
|
||
if i >= n - 1: i = n - 2; dx = 1.0
|
||
if j >= n - 1: j = n - 2; dy = 1.0
|
||
|
||
# Récupération des 4 valeurs voisines
|
||
v00 = matrix[i][j]
|
||
v10 = matrix[i + 1][j]
|
||
v01 = matrix[i][j + 1]
|
||
v11 = matrix[i + 1][j + 1]
|
||
|
||
# Interpolation bilinéaire
|
||
interpolated = (
|
||
(1 - dx) * (1 - dy) * v00 +
|
||
dx * (1 - dy) * v10 +
|
||
(1 - dx) * dy * v01 +
|
||
dx * dy * v11
|
||
)
|
||
return interpolated
|
||
|
||
def approx_val_from_bins(self, row_label, col_label):
|
||
"""
|
||
Renvoie une approximation de la valeur à partir des labels binaires (e.g. B5, H1)
|
||
en utilisant une interpolation simple basée sur les indices.
|
||
|
||
Parameters:
|
||
matrix_df (pd.DataFrame): Matrice avec les labels binaires en index et colonnes.
|
||
row_label (str): Label de la ligne (ex: 'B3').
|
||
col_label (str): Label de la colonne (ex: 'H2').
|
||
|
||
Returns:
|
||
float: Valeur approchée si possible, sinon NaN.
|
||
"""
|
||
|
||
# Vérification des labels
|
||
if row_label not in self.matrix_df.index or col_label not in self.matrix_df.columns:
|
||
return np.nan
|
||
|
||
# Récupération des labels ordonnés
|
||
ordered_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
|
||
label_to_index = {label: i for i, label in enumerate(ordered_labels)}
|
||
|
||
# Index correspondant
|
||
row_idx = label_to_index.get(row_label)
|
||
col_idx = label_to_index.get(col_label)
|
||
|
||
# Extraction de la matrice numérique
|
||
numeric_matrix = self.matrix_df.reindex(index=ordered_labels, columns=ordered_labels).values
|
||
|
||
# Approximation directe (aucune interpolation complexe ici, juste une lecture)
|
||
return numeric_matrix[row_idx, col_idx]
|
||
|
||
|
||
# @property
|
||
# def protections(self):
|
||
# return [
|
||
# {
|
||
# "method": "CooldownPeriod",
|
||
# "stop_duration_candles": 12
|
||
# }
|
||
# # {
|
||
# # "method": "MaxDrawdown",
|
||
# # "lookback_period_candles": self.lookback.value,
|
||
# # "trade_limit": self.trade_limit.value,
|
||
# # "stop_duration_candles": self.protection_stop.value,
|
||
# # "max_allowed_drawdown": self.protection_max_allowed_dd.value,
|
||
# # "only_per_pair": False
|
||
# # },
|
||
# # {
|
||
# # "method": "StoplossGuard",
|
||
# # "lookback_period_candles": 24,
|
||
# # "trade_limit": 4,
|
||
# # "stop_duration_candles": self.protection_stoploss_stop.value,
|
||
# # "only_per_pair": False
|
||
# # },
|
||
# # {
|
||
# # "method": "StoplossGuard",
|
||
# # "lookback_period_candles": 24,
|
||
# # "trade_limit": 4,
|
||
# # "stop_duration_candles": 2,
|
||
# # "only_per_pair": False
|
||
# # },
|
||
# # {
|
||
# # "method": "LowProfitPairs",
|
||
# # "lookback_period_candles": 6,
|
||
# # "trade_limit": 2,
|
||
# # "stop_duration_candles": 60,
|
||
# # "required_profit": 0.02
|
||
# # },
|
||
# # {
|
||
# # "method": "LowProfitPairs",
|
||
# # "lookback_period_candles": 24,
|
||
# # "trade_limit": 4,
|
||
# # "stop_duration_candles": 2,
|
||
# # "required_profit": 0.01
|
||
# # }
|
||
# ] |