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120 lines
4.9 KiB
Python
120 lines
4.9 KiB
Python
from typing import Dict
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def directional_strategy_template(strategy_cls_name: str) -> str:
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strategy_config_cls_name = f"{strategy_cls_name}Config"
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sma_config_text = "{self.config.sma_length}"
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return f"""import pandas_ta as ta
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from pydantic import BaseModel, Field
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from quants_lab.strategy.directional_strategy_base import DirectionalStrategyBase
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class {strategy_config_cls_name}(BaseModel):
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exchange: str = Field(default="binance_perpetual")
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trading_pair: str = Field(default="ETH-USDT")
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interval: str = Field(default="1h")
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sma_length: int = Field(default=20, ge=10, le=200)
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# ... Add more fields here
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class {strategy_cls_name}(DirectionalStrategyBase[{strategy_config_cls_name}]):
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def get_raw_data(self):
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# The method get candles will search for the data in the folder data/candles
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# If the data is not there, you can use the candles downloader to get the data
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df = self.get_candles(
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exchange=self.config.exchange,
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trading_pair=self.config.trading_pair,
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interval=self.config.interval,
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)
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return df
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def preprocessing(self, df):
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df.ta.sma(length=self.config.sma_length, append=True)
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# ... Add more indicators here
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# ... Check https://github.com/twopirllc/pandas-ta#indicators-by-category for more indicators
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# ... Use help(ta.indicator_name) to get more info
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return df
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def predict(self, df):
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# Generate long and short conditions
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long_cond = (df['close'] > df[f'SMA_{sma_config_text}'])
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short_cond = (df['close'] < df[f'SMA_{sma_config_text}'])
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# Choose side
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df['side'] = 0
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df.loc[long_cond, 'side'] = 1
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df.loc[short_cond, 'side'] = -1
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return df
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"""
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def get_optuna_suggest_str(field_name: str, properties: Dict):
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map_by_type = {
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"number": "trial.suggest_float",
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"integer": "trial.suggest_int",
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"string": "trial.suggest_categorical",
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}
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config_num = f"('{field_name}', {properties.get('minimum', '_')}, {properties.get('maximum', '_')})"
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config_cat = f"('{field_name}', ['{properties.get('default', '_')}',])"
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optuna_trial_str = map_by_type[properties["type"]] + config_num if properties["type"] != "string" \
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else map_by_type[properties["type"]] + config_cat
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return f"{field_name}={optuna_trial_str}"
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def strategy_optimization_template(strategy_info: dict):
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strategy_cls = strategy_info["class"]
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strategy_config = strategy_info["config"]
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strategy_module = strategy_info["module"]
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field_schema = strategy_config.schema()["properties"]
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fields_str = [get_optuna_suggest_str(field_name, properties) for field_name, properties in field_schema.items()]
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fields_str = "".join([f" {field_str},\n" for field_str in fields_str])
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return f"""import traceback
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from optuna import TrialPruned
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from quants_lab.strategy.experiments.{strategy_module} import {strategy_cls.__name__}, {strategy_config.__name__}
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from quants_lab.strategy.strategy_analysis import StrategyAnalysis
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def objective(trial):
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try:
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config = {strategy_config.__name__}(
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{fields_str}
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)
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strategy = {strategy_cls.__name__}(config=config)
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market_data, positions = strategy.run_backtesting(
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start='2021-04-01',
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order_amount=50,
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leverage=20,
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initial_portfolio=100,
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take_profit_multiplier=trial.suggest_float("take_profit_multiplier", 1.0, 3.0),
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stop_loss_multiplier=trial.suggest_float("stop_loss_multiplier", 1.0, 3.0),
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time_limit=60 * 60 * trial.suggest_int("time_limit", 1, 24),
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std_span=None,
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)
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strategy_analysis = StrategyAnalysis(
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positions=positions,
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)
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trial.set_user_attr("net_profit_usd", strategy_analysis.net_profit_usd())
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trial.set_user_attr("net_profit_pct", strategy_analysis.net_profit_pct())
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trial.set_user_attr("max_drawdown_usd", strategy_analysis.max_drawdown_usd())
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trial.set_user_attr("max_drawdown_pct", strategy_analysis.max_drawdown_pct())
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trial.set_user_attr("sharpe_ratio", strategy_analysis.sharpe_ratio())
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trial.set_user_attr("accuracy", strategy_analysis.accuracy())
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trial.set_user_attr("total_positions", strategy_analysis.total_positions())
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trial.set_user_attr("win_signals", strategy_analysis.win_signals().shape[0])
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trial.set_user_attr("loss_signals", strategy_analysis.loss_signals().shape[0])
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trial.set_user_attr("profit_factor", strategy_analysis.profit_factor())
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trial.set_user_attr("duration_in_hours", strategy_analysis.duration_in_minutes() / 60)
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trial.set_user_attr("avg_trading_time_in_hours", strategy_analysis.avg_trading_time_in_minutes() / 60)
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trial.set_user_attr("config", config.dict())
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return strategy_analysis.net_profit_pct()
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except Exception as e:
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traceback.print_exc()
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raise TrialPruned()
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"""
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