# source: https://raw.githubusercontent.com/mlsys-io/PortfolioBench/d9c11d8324d3581979e67491df4b7c0cadde9c7b/user_data/strategies/min_var.py from freqtrade.persistence.trade_model import Trade from freqtrade.strategy import IStrategy from pandas import DataFrame import pandas as pd import numpy as np from datetime import datetime from typing import Optional import logging logger = logging.getLogger(__name__) class Github_mlsys_io_PortfolioBench__min_var__20260407_162651(IStrategy): """ Minimum Variance Portfolio strategy with stake allocation. - Computes daily returns over a lookback window. - Calculates minimum-variance weights. - Rebalances monthly (on the 1st day of each month). """ INTERFACE_VERSION = 3 timeframe = "1d" stoploss = -1.0 LOOKBACK = 30 REBALANCE_DATE = 1 process_only_new_candles = True startup_candle_count = 30 position_adjustment_enable = True max_entry_position_adjustment = -1 minimal_roi = {} def _compute_mvp_weights(self, price_data: pd.DataFrame) -> pd.DataFrame: n_cols = len(price_data.columns) pairs = price_data.columns.tolist() n_rows = len(price_data) fallback = np.ones(n_cols) / n_cols weights_history = np.zeros((n_rows, n_cols)) for i in range(n_rows): if i < self.LOOKBACK: weights_history[i] = fallback continue window = price_data.iloc[i - self.LOOKBACK:i] returns = window.pct_change().dropna() if returns.empty or len(returns) < 2: weights_history[i] = fallback continue try: cov_matrix = returns.cov() inv_cov = np.linalg.pinv(cov_matrix) ones = np.ones(n_cols) w = (inv_cov @ ones) / (ones.T @ inv_cov @ ones) w = np.maximum(w, 0) total = w.sum() weights_history[i] = w / total if total > 0 else fallback except Exception: weights_history[i] = fallback return pd.DataFrame(weights_history, index=price_data.index, columns=pairs) def _get_first_trading_day(self, dataframe: DataFrame) -> pd.Series: """ Returns a boolean Series that is True on the first available trading day of each month in the dataframe. """ dates = pd.to_datetime(dataframe['date']) if dates.dt.tz is not None: dates = dates.dt.tz_convert(None) else: dates = dates.dt.tz_localize(None) month_year = dates.dt.to_period('M') # Mark the first occurrence of each month first_trading_day = ~month_year.duplicated(keep='first') return first_trading_day def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if not hasattr(self, 'mvp_weights_cache'): self.mvp_weights_cache = {} current_pair = metadata['pair'] dataframe['target_weight'] = 0.0 if self.dp: price_dict = {} for pair in self.dp.current_whitelist(): inf_df = self.dp.get_pair_dataframe(pair, self.timeframe) price_dict[pair] = inf_df['close'] aligned_prices = pd.DataFrame(price_dict).dropna() if not aligned_prices.empty: cache_key = ( str(aligned_prices.index[0]) + str(aligned_prices.index[-1]) + str(len(self.dp.current_whitelist())) ) if cache_key not in self.mvp_weights_cache: logger.info(f"Calculating MVP weights for {len(aligned_prices)} candles across {len(price_dict)} pairs...") weights_df = self._compute_mvp_weights(aligned_prices) self.mvp_weights_cache[cache_key] = weights_df weights = self.mvp_weights_cache[cache_key] if current_pair in weights.columns: dataframe['target_weight'] = dataframe.index.map(weights[current_pair]) else: dataframe['target_weight'] = 0.0 dataframe["rebalance"] = self._get_first_trading_day(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'enter_long'] = ( (dataframe['target_weight'] > 0) & (dataframe['rebalance'] == True) ).astype(int) return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = ( (dataframe['target_weight'] == 0) & (dataframe['rebalance'] == True) ).astype(int) return dataframe def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe.empty: return proposed_stake target_weight = dataframe.iloc[-1].get('target_weight', 0.0) if not np.isfinite(target_weight) or target_weight <= 0: return proposed_stake total_wallet = self._get_total_wallet(pair, current_time, current_rate) return total_wallet * target_weight def _get_total_wallet(self, pair: str, current_time: datetime, current_rate: float) -> float: if self.wallets: total_wallet = self.wallets.get_free(self.config['stake_currency']) for t in Trade.get_open_trades(): if t.pair == pair: total_wallet += t.amount * current_rate else: try: pair_df, _ = self.dp.get_analyzed_dataframe(t.pair, self.timeframe) rate = pair_df.loc[pair_df['date'] == current_time, 'close'].values[0] total_wallet += t.amount * rate except (IndexError, KeyError): total_wallet += t.stake_amount else: total_wallet = self.config['dry_run_wallet'] return total_wallet def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) if dataframe.empty: return None if dataframe.iloc[-1].get('rebalance', False) == False: return None target_weight = dataframe.iloc[-1].get('target_weight', 0.0) if not np.isfinite(target_weight) or target_weight <= 0: return None total_wallet = self._get_total_wallet(trade.pair, current_time, current_rate) target_size = target_weight * total_wallet current_position_value = trade.amount * current_rate diff = target_size - current_position_value if abs(diff) < min_stake: return None if diff < 0: diff = max(diff, -trade.stake_amount) if abs(diff) < min_stake: return None return float(diff)