633b033f4d
- Infra: Freqtrade (futures dry-run) + Redis + dashboard + Docker Compose - Couche IA: ai_analyzer (Claude via abonnement, MCP TradingView, backfill biais) - Stratégies: SampleStrategy, AiBiasStrategy, IchimokuLS (long/short, validée train/test + données vierges + walk-forward), MTFIchimoku, variantes hyperopt - Arbitrage CEX (dry-run), backtesting, walk-forward, volatility targeting - IchimokuLS en dry-run live (config_live.json) Claude-Session: https://claude.ai/code/session_01VHETcFacdnDhQzthLpdYFR
65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
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"""
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LeveragedStrategy — DÉMONSTRATION du risque du levier (NE PAS utiliser en réel).
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Même logique technique que SampleStrategy, mais en futures avec levier 10x.
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But : montrer empiriquement que le levier crée des semaines à +10 % ET des semaines
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catastrophiques — donc « +10 % chaque semaine » reste impossible, et le levier ruine.
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"""
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from __future__ import annotations
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import talib.abstract as ta
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from pandas import DataFrame
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from freqtrade.strategy import IStrategy
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class LeveragedStrategy(IStrategy):
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INTERFACE_VERSION = 3
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timeframe = "1h"
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can_short = False
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minimal_roi = {"0": 0.05, "120": 0.03, "360": 0.01, "720": 0}
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stoploss = -0.10
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trailing_stop = True
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trailing_stop_positive = 0.02
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trailing_stop_positive_offset = 0.03
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trailing_only_offset_is_reached = True
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startup_candle_count: int = 50
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process_only_new_candles = True
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use_exit_signal = True
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def leverage(self, pair, current_time, current_rate, proposed_leverage,
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max_leverage, entry_tag, side, **kwargs) -> float:
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return min(5.0, max_leverage) # 5x
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=9)
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dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=21)
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe["ema_fast"] > dataframe["ema_slow"])
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& (dataframe["ema_fast"].shift(1) <= dataframe["ema_slow"].shift(1))
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& (dataframe["rsi"] < 70)
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& (dataframe["volume"] > 0)
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),
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"enter_long",
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] = 1
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe["ema_fast"] < dataframe["ema_slow"])
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& (dataframe["ema_fast"].shift(1) >= dataframe["ema_slow"].shift(1))
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& (dataframe["volume"] > 0)
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),
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"exit_long",
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] = 1
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return dataframe
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