# source: https://raw.githubusercontent.com/DOUGLASGUEDESATRIA/OSIRIS_TRADE/fe3c09567f3798d1cad6d7a306fa4f6131d91e17/user_data/strategies/OsirisFlow.py """ OSIRIS FLOW STRATEGY v1.0 — Real Order Flow Day Trading ========================================================= Usa tick data real (aggTrades da Binance) processado em features de order flow. NÃO usa proxies OHLCV — usa delta, CVD, big trades, absorption, sweeps REAIS. FEATURES DE ORDER FLOW (derivados de tick data): 1. Delta (buy_vol - sell_vol) — pressão real compradora/vendedora 2. CVD (Cumulative Volume Delta) — tendência de fluxo acumulado 3. Buy Ratio — fração do volume que é compra agressiva 4. Big Trades Imbalance — desbalanceamento de trades grandes (institucional) 5. Delta Z-Score — delta normalizado para comparação entre períodos 6. Volume Spike — anomalias de volume (entrada institucional) 7. Absorption — alto volume sem movimento de preço (parede institucional) 8. Sweep Up/Down — liquidity grabs (stop hunting) 9. Delta Divergence — preço sobe mas delta negativo (ou vice versa) 10. Aggression Ratio — buy_qty/sell_qty (urgência direcional) 11. Trade Intensity — trades/segundo (atividade do mercado) FILOSOFIA: - Day trade: entries e exits em minutos/horas, não dias - 10 trades/dia target via high-frequency order flow signals - SL apertado (0.5-1%), TP 0.3-1%, trailing para runs - Edge vem de dados que 99% dos traders não tem acesso 100% proprietário. Desenvolvido exclusivamente para OSIRIS. """ import logging import numpy as np import pandas as pd from pathlib import Path from pandas import DataFrame from typing import Optional from freqtrade.strategy import IStrategy from freqtrade.strategy import DecimalParameter, IntParameter from freqtrade.persistence import Trade import talib.abstract as ta logger = logging.getLogger(__name__) # Path to order flow feather data ORDERFLOW_PATH = Path(__file__).parent.parent / "data" / "orderflow" / "BTCUSDT-orderflow-5m.feather" class Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisFlow__20260329_060843(IStrategy): """ OSIRIS Flow — Real order flow day trading. """ timeframe = "5m" can_short = True # Day trade exits — wider range, let hyperopt decide minimal_roi = { "0": 0.02, "15": 0.01, "45": 0.005, "90": 0.002, "180": 0, } stoploss = -0.03 # -3% max loss per trade (wider for BTC) trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.01 trailing_only_offset_is_reached = True use_custom_stoploss = False process_only_new_candles = True startup_candle_count: int = 30 # ─── Hyperopt Parameters ───────────────────────────────────────────── # Long entry thresholds buy_delta_zscore = DecimalParameter(0.5, 3.0, default=1.2, space="buy", optimize=True) buy_buy_ratio = DecimalParameter(0.52, 0.75, default=0.58, space="buy", optimize=True) buy_big_imbalance = DecimalParameter(0.05, 0.5, default=0.15, space="buy", optimize=True) buy_vol_spike = DecimalParameter(1.0, 3.0, default=1.5, space="buy", optimize=True) buy_aggression = DecimalParameter(1.1, 3.0, default=1.3, space="buy", optimize=True) buy_score_min = IntParameter(5, 10, default=7, space="buy", optimize=True) # Short entry thresholds sell_delta_zscore = DecimalParameter(0.5, 3.0, default=1.2, space="buy", optimize=True) sell_buy_ratio = DecimalParameter(0.25, 0.48, default=0.42, space="buy", optimize=True) sell_big_imbalance = DecimalParameter(-0.5, -0.05, default=-0.15, space="buy", optimize=True) sell_aggression = DecimalParameter(0.3, 0.9, default=0.7, space="buy", optimize=True) # Exit parameters exit_delta_reversal = DecimalParameter(0.5, 2.5, default=1.0, space="sell", optimize=True) exit_absorption_thr = DecimalParameter(1.5, 4.0, default=2.0, space="sell", optimize=True) # Order flow data cache _orderflow_df: Optional[pd.DataFrame] = None def _load_orderflow(self) -> pd.DataFrame: """Load and cache order flow feather data.""" if self._orderflow_df is None: if ORDERFLOW_PATH.exists(): self._orderflow_df = pd.read_feather(ORDERFLOW_PATH) # Ensure datetime index if 'date' in self._orderflow_df.columns: self._orderflow_df['date'] = pd.to_datetime(self._orderflow_df['date']) self._orderflow_df = self._orderflow_df.set_index('date') logger.info(f"Loaded order flow data: {len(self._orderflow_df)} candles " f"from {self._orderflow_df.index.min()} to {self._orderflow_df.index.max()}") elif ORDERFLOW_PATH.is_symlink(): target = ORDERFLOW_PATH.resolve() if target.exists(): self._orderflow_df = pd.read_feather(target) if 'date' in self._orderflow_df.columns: self._orderflow_df['date'] = pd.to_datetime(self._orderflow_df['date']) self._orderflow_df = self._orderflow_df.set_index('date') logger.info(f"Loaded order flow data (symlink): {len(self._orderflow_df)} candles") else: logger.error(f"Order flow data not found: {ORDERFLOW_PATH} -> {target}") self._orderflow_df = pd.DataFrame() else: logger.error(f"Order flow data not found: {ORDERFLOW_PATH}") self._orderflow_df = pd.DataFrame() return self._orderflow_df def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Merge order flow features into the Freqtrade dataframe.""" of = self._load_orderflow() if of.empty: logger.warning("No order flow data — all features will be NaN") for col in ['delta', 'cvd', 'buy_ratio', 'buy_volume', 'sell_volume', 'trade_intensity', 'big_imbalance', 'delta_zscore', 'vol_spike', 'absorption', 'sweep_up', 'sweep_down', 'delta_divergence', 'aggression', 'big_buys', 'big_sells', 'vwap']: dataframe[col] = 0 else: # Merge by timestamp dataframe['date_key'] = pd.to_datetime(dataframe['date']) of_reset = of.reset_index() of_reset.rename(columns={'index': 'date_key'} if 'index' in of_reset.columns else {}, inplace=True) if 'date_key' not in of_reset.columns and of.index.name: of_reset = of_reset.rename(columns={of.index.name: 'date_key'}) # Select only order flow columns flow_cols = ['delta', 'cvd', 'buy_ratio', 'buy_volume', 'sell_volume', 'trade_intensity', 'big_imbalance', 'delta_zscore', 'vol_spike', 'absorption', 'sweep_up', 'sweep_down', 'delta_divergence', 'aggression', 'big_buys', 'big_sells', 'vwap'] available_cols = [c for c in flow_cols if c in of_reset.columns] of_subset = of_reset[['date_key'] + available_cols].copy() of_subset['date_key'] = pd.to_datetime(of_subset['date_key']).dt.tz_localize(None) dataframe['date_key'] = pd.to_datetime(dataframe['date_key']).dt.tz_localize(None) dataframe = dataframe.merge(of_subset, on='date_key', how='left', suffixes=('', '_of')) dataframe.drop(columns=['date_key'], inplace=True) # Fill NaN with neutral values for col in available_cols: if col in dataframe.columns: if col == 'buy_ratio': dataframe[col] = dataframe[col].fillna(0.5) elif col == 'aggression': dataframe[col] = dataframe[col].fillna(1.0) else: dataframe[col] = dataframe[col].fillna(0) matched = dataframe['delta'].ne(0).sum() logger.info(f"Order flow merge: {matched}/{len(dataframe)} candles matched " f"({matched/len(dataframe)*100:.1f}%)") # ─── Derived indicators from order flow ────────────────────────── # CVD momentum (rate of change of CVD) dataframe['cvd_roc'] = dataframe['cvd'].pct_change(5) * 100 # Delta momentum (sum of last 3 deltas) dataframe['delta_sum3'] = dataframe['delta'].rolling(3).sum() dataframe['delta_sum5'] = dataframe['delta'].rolling(5).sum() # Smoothed buy ratio (EMA of buy_ratio) dataframe['buy_ratio_ema'] = dataframe['buy_ratio'].ewm(span=10).mean() # Big trades cumulative (directional big trade flow) dataframe['big_flow'] = (dataframe['big_buys'] - dataframe['big_sells']).rolling(5).sum() # Absorption strength (rolling max) dataframe['absorption_peak'] = dataframe['absorption'].rolling(10).max() # Sweep preceded by large volume (confirmation) dataframe['sweep_confirmed_up'] = ( (dataframe['sweep_up'] > 1.5) & (dataframe['vol_spike'] > 1.5) ).astype(int) dataframe['sweep_confirmed_down'] = ( (dataframe['sweep_down'] > 1.5) & (dataframe['vol_spike'] > 1.5) ).astype(int) # Trade intensity z-score ti_mean = dataframe['trade_intensity'].rolling(20).mean() ti_std = dataframe['trade_intensity'].rolling(20).std().replace(0, np.nan) dataframe['ti_zscore'] = (dataframe['trade_intensity'] - ti_mean) / ti_std # VWAP deviation (price relative to VWAP) dataframe['vwap_dev'] = np.where( dataframe['vwap'] > 0, (dataframe['close'] - dataframe['vwap']) / dataframe['vwap'] * 100, 0 ) # Simple trend filter (EMA 20) dataframe['ema20'] = ta.EMA(dataframe['close'], timeperiod=20) dataframe['ema50'] = ta.EMA(dataframe['close'], timeperiod=50) dataframe['trend'] = np.where(dataframe['ema20'] > dataframe['ema50'], 1, -1) # RSI for overbought/oversold filter dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Score-based entry using real order flow signals.""" # ─── LONG SCORING ──────────────────────────────────────────────── score_long = pd.Series(0, index=dataframe.index, dtype=int) # 1. Delta Z-Score positive (buying pressure above normal) score_long += np.where(dataframe['delta_zscore'] > self.buy_delta_zscore.value, 1, 0) # 2. Buy ratio high (more aggressive buyers) score_long += np.where(dataframe['buy_ratio'] > self.buy_buy_ratio.value, 1, 0) # 3. Buy ratio EMA trending up score_long += np.where(dataframe['buy_ratio_ema'] > self.buy_buy_ratio.value, 1, 0) # 4. Big trades imbalance positive (institutions buying) score_long += np.where(dataframe['big_imbalance'] > self.buy_big_imbalance.value, 1, 0) # 5. Volume spike with buying (smart money entry) score_long += np.where( (dataframe['vol_spike'] > self.buy_vol_spike.value) & (dataframe['buy_ratio'] > 0.52), 1, 0) # 6. Aggression ratio high (urgent buying) score_long += np.where(dataframe['aggression'] > self.buy_aggression.value, 1, 0) # 7. Delta sum positive (sustained buying over 3 candles) score_long += np.where(dataframe['delta_sum3'] > 0, 1, 0) # 8. CVD trending up (cumulative buying trend) score_long += np.where(dataframe['cvd_roc'] > 0.1, 1, 0) # 9. Big institutional flow positive score_long += np.where(dataframe['big_flow'] > 10, 1, 0) # 10. Trade intensity elevated (active market, not dead) score_long += np.where(dataframe['ti_zscore'] > 0.5, 1, 0) # 11. No bearish sweep (not a bull trap) score_long += np.where(dataframe['sweep_confirmed_down'].shift(1) == 0, 1, 0) # Entry condition dataframe['enter_long'] = ( (score_long >= self.buy_score_min.value) & (dataframe['volume'] > 0) ).astype(int) # ─── SHORT SCORING ─────────────────────────────────────────────── score_short = pd.Series(0, index=dataframe.index, dtype=int) # 1. Delta Z-Score negative (selling pressure above normal) score_short += np.where(dataframe['delta_zscore'] < -self.sell_delta_zscore.value, 1, 0) # 2. Buy ratio low (more aggressive sellers) score_short += np.where(dataframe['buy_ratio'] < self.sell_buy_ratio.value, 1, 0) # 3. Buy ratio EMA trending down score_short += np.where(dataframe['buy_ratio_ema'] < self.sell_buy_ratio.value, 1, 0) # 4. Big trades imbalance negative (institutions selling) score_short += np.where(dataframe['big_imbalance'] < self.sell_big_imbalance.value, 1, 0) # 5. Volume spike with selling score_short += np.where( (dataframe['vol_spike'] > self.buy_vol_spike.value) & (dataframe['buy_ratio'] < 0.48), 1, 0) # 6. Aggression ratio low (urgent selling) score_short += np.where(dataframe['aggression'] < self.sell_aggression.value, 1, 0) # 7. Delta sum negative (sustained selling over 3 candles) score_short += np.where(dataframe['delta_sum3'] < 0, 1, 0) # 8. CVD trending down score_short += np.where(dataframe['cvd_roc'] < -0.1, 1, 0) # 9. Big institutional flow negative score_short += np.where(dataframe['big_flow'] < -10, 1, 0) # 10. Trade intensity elevated score_short += np.where(dataframe['ti_zscore'] > 0.5, 1, 0) # 11. No bullish sweep score_short += np.where(dataframe['sweep_confirmed_up'].shift(1) == 0, 1, 0) dataframe['enter_short'] = ( (score_short >= self.buy_score_min.value) & (dataframe['volume'] > 0) ).astype(int) # Store scores for analysis dataframe['score_long'] = score_long dataframe['score_short'] = score_short return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Exit on order flow reversal signals.""" # Exit long when selling pressure appears dataframe['exit_long'] = ( (dataframe['delta_zscore'] < -self.exit_delta_reversal.value) | (dataframe['absorption'] > self.exit_absorption_thr.value) ).astype(int) # Exit short when buying pressure appears dataframe['exit_short'] = ( (dataframe['delta_zscore'] > self.exit_delta_reversal.value) | (dataframe['absorption'] > self.exit_absorption_thr.value) ).astype(int) return dataframe