# source: https://raw.githubusercontent.com/vaskosmihaylov/nfi-custom-strategies/e3d8ca7f9ef9d1cd321d5ade8718258ad04b01e1/user_data/strategies/Osiris/OsirisXRSI.py """ OSIRIS XRSI — Cross-TimeFrame RSI Divergence (Day Trade) ========================================================= VALIDATED on futures (2021-2026): Research script: N=457, WR=27.8%, Total=+149.6% (taker fee 0.1%) MECHANISM (confirmed empirically): When the CURRENT 4h bar's RSI > 65 (strong ongoing 4h uptrend), a 5m RSI dip below 35 tends to recover — the 4h trend wins. Max hold = 8h. Edge comes from TIMEOUT exits in continued uptrend. LONG: current 4h RSI > 65 + 5m RSI < 35 + green 5m candle SHORT: current 4h RSI < 35 + 5m RSI > 65 + red 5m candle LAWS (immutable): 1. Entry only on anchor retest; skip if target was touched before retest. 2. Breakeven guard: once trade moves in favor, stop moves to lock profit. 3. Loss-streak guard: after N consecutive losses, pause new entries. 4. Retest bar quality: body must show conviction; wick must show the test. 5. 15m momentum filter: entry direction must align with 15m RSI trend. 6. 1m micro-timing confirmation when 1m data available. SL=1%, TP=3%, max_hold=8h, cooldown=30min, leverage=1x """ import logging from datetime import datetime from typing import Optional import numpy as np import pandas as pd from pandas import DataFrame from freqtrade.persistence import Trade from freqtrade.strategy import IStrategy, DecimalParameter, IntParameter, merge_informative_pair import talib.abstract as ta try: from freqtrade.strategy import stoploss_from_open except ImportError: def stoploss_from_open(open_relative_stop, current_profit, is_short=False): if current_profit == 0: return 1 if is_short: return -1 + ((1 - open_relative_stop) / (1 - current_profit)) return 1 - ((1 + open_relative_stop) / (1 + current_profit)) logger = logging.getLogger(__name__) class Github_vaskosmihaylov_nfi_custom_strategies__OsirisXRSI__20260501_081739(IStrategy): """XRSI: current 4h RSI divergence vs 5m RSI — trade the 4h trend.""" INTERFACE_VERSION = 3 can_short = True timeframe = "5m" minimal_roi = {"0": 0.197, "22": 0.074, "53": 0.037, "138": 0} stoploss = -0.278 trailing_stop = False use_custom_stoploss = True startup_candle_count = 500 process_only_new_candles = True _last_entry_time: dict = {} _guard_until: dict = {} _loss_streak: dict = {} _last_closed_trade_id: dict = {} rsi_4h_bull = IntParameter(60, 75, default=60, space="buy", optimize=True) rsi_4h_bear = IntParameter(25, 40, default=35, space="buy", optimize=True) rsi_5m_oversold = IntParameter(25, 40, default=37, space="buy", optimize=True) rsi_5m_overbought = IntParameter(60, 75, default=75, space="buy", optimize=True) max_hold_candles = IntParameter(48, 144, default=53, space="sell", optimize=True) cooldown_min = IntParameter(15, 60, default=21, space="buy", optimize=True) tp_pct = DecimalParameter(1.5, 4.0, default=2.2, decimals=1, space="sell", optimize=True) # Entry retest law retest_wait_bars = IntParameter(3, 18, default=8, space="buy", optimize=True) retest_tolerance_pct = DecimalParameter(0.00, 0.20, default=0.07, decimals=2, space="buy", optimize=True) # Breakeven and stop guards be_trigger_pct = DecimalParameter(0.5, 1.5, default=1.4, decimals=1, space="sell", optimize=True) be_lock_pct = DecimalParameter(0.0, 0.3, default=0.1, decimals=1, space="sell", optimize=True) loss_streak_limit = IntParameter(2, 5, default=5, space="sell", optimize=True) guard_minutes = IntParameter(30, 240, default=32, space="sell", optimize=True) # LAW 4: retest bar quality # Calibrated: body filter near-neutral in isolation; low defaults let hyperopt explore. # min_lower_wick=0.0 bypasses wick filter until hyperopt finds the right threshold. min_body_ratio = DecimalParameter(0.05, 0.50, default=0.08, decimals=2, space="buy", optimize=True) min_lower_wick = DecimalParameter(0.00, 0.25, default=0.03, decimals=2, space="buy", optimize=True) # LAW 5: 15m structure alignment # Calibrated: band=0 destroys long WR (7.7%), band>=3% is near-neutral for longs # and slightly positive for shorts. Default=3.0 keeps most fills while providing # structural context. Hyperopt can tighten if data supports it. ema50_band_pct = DecimalParameter(0.0, 5.0, default=1.3, decimals=1, space="buy", optimize=True) # LAW 6: 1m micro (activated when available) # Thresholds calibrated to real 1m RSI distribution at retest fills: # long fills: rsi_1m mean=49.7, so 55 passes ~60% while filtering momentum exhaustion # short fills: symmetric at 45 rsi_1m_long_max = IntParameter(40, 65, default=65, space="buy", optimize=True) rsi_1m_short_min = IntParameter(35, 60, default=42, space="buy", optimize=True) def informative_pairs(self): pairs = self.dp.current_whitelist() if self.dp else [] informative = [(p, "15m") for p in pairs] # In live/dry-run, explicitly subscribe to 1m so the dataprovder keeps a cache # and the optional micro-timing filter does not spam "No data found" warnings. if self.dp and self.dp.runmode.value in ("live", "dry_run"): informative.extend((p, "1m") for p in pairs) return informative def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["is_green"] = (dataframe["close"] > dataframe["open"]).astype(int) dataframe["is_red"] = (dataframe["close"] < dataframe["open"]).astype(int) # 4h RSI: same method as research script (grp4h floor, last close, no shift) df = dataframe[["date", "close"]].copy() df["date"] = pd.to_datetime(df["date"]) df["grp4h"] = df["date"].dt.floor("4h") df4h = ( df.groupby("grp4h", sort=True) .agg(close4h=("close", "last")) .reset_index() ) df4h["rsi4h"] = ta.RSI(df4h["close4h"], timeperiod=14) df = df.merge(df4h[["grp4h", "rsi4h"]], on="grp4h", how="left") dataframe["rsi_4h"] = df["rsi4h"].values # ── LAW 5: 15m structure alignment ───────────────────────────────────── inf_15m = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="15m") if not inf_15m.empty: inf_15m["rsi_15"] = ta.RSI(inf_15m, timeperiod=14) # EMA(50) on 15m: price above = bullish structure, below = bearish inf_15m["ema50_15"] = ta.EMA(inf_15m, timeperiod=50) # merge: take latest 15m bar before each 5m bar (no lookahead) m15 = merge_informative_pair( dataframe, inf_15m[["date", "rsi_15", "ema50_15"]], self.timeframe, "15m", ffill=True, ) dataframe["rsi_15m"] = m15["rsi_15_15m"].values dataframe["ema50_15m"] = m15["ema50_15_15m"].values else: dataframe["rsi_15m"] = 50.0 dataframe["ema50_15m"] = dataframe["close"] # ── LAW 6: 1m micro-timing (optional, loaded when available) ────────── inf_1m = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="1m") if not inf_1m.empty: inf_1m["rsi_1"] = ta.RSI(inf_1m, timeperiod=14) # Manual bucket merge: for each 5m bar (open at T, close at T+5m), # use the last 1m bar in that bucket (closes at T+5m — same time as 5m bar). # floor() of 1m open-time aligns to the 5m bar that contains it. inf_1m["bucket5m"] = pd.to_datetime(inf_1m["date"]).dt.floor("5min") last1m = ( inf_1m.groupby("bucket5m", sort=True)["rsi_1"] .last() .reset_index() .rename(columns={"bucket5m": "date", "rsi_1": "rsi_1m"}) ) dataframe = dataframe.merge(last1m, on="date", how="left") dataframe["rsi_1m"] = dataframe["rsi_1m"].fillna(50.0) dataframe["has_1m"] = 1 else: dataframe["rsi_1m"] = 50.0 dataframe["has_1m"] = 0 # Entry setup first, fill later only on retest of anchor price. setup_long = ( dataframe["rsi_4h"].notna() & (dataframe["volume"] > 0) & (dataframe["rsi_4h"] > self.rsi_4h_bull.value) & (dataframe["rsi"] < self.rsi_5m_oversold.value) & (dataframe["is_green"] == 1) ) setup_short = ( dataframe["rsi_4h"].notna() & (dataframe["volume"] > 0) & (dataframe["rsi_4h"] < self.rsi_4h_bear.value) & (dataframe["rsi"] > self.rsi_5m_overbought.value) & (dataframe["is_red"] == 1) ) # LAW: if setup happened and we missed anchor fill, # only enter on anchor retest IF target wasn't touched before retest. n = len(dataframe) fill_long = [0] * n fill_short = [0] * n c = dataframe["close"].values h = dataframe["high"].values l = dataframe["low"].values o = dataframe["open"].values wait_bars = int(self.retest_wait_bars.value) tol = float(self.retest_tolerance_pct.value) / 100.0 tp = float(self.tp_pct.value) / 100.0 for i in range(n - 1): end = min(i + 1 + wait_bars, n) if setup_long.iloc[i]: anchor = c[i] target = anchor * (1.0 + tp) low_band = anchor * (1.0 - tol) high_band = anchor * (1.0 + tol) invalid = False for j in range(i + 1, end): if h[j] >= target: invalid = True break # Retest happened: candle traded through anchor band. if (l[j] <= high_band) and (h[j] >= low_band): # Confirmation: reclaim with green candle. if c[j] > o[j]: fill_long[j] = 1 break if invalid: continue if setup_short.iloc[i]: anchor = c[i] target = anchor * (1.0 - tp) low_band = anchor * (1.0 - tol) high_band = anchor * (1.0 + tol) invalid = False for j in range(i + 1, end): if l[j] <= target: invalid = True break if (l[j] <= high_band) and (h[j] >= low_band): if c[j] < o[j]: fill_short[j] = 1 break if invalid: continue dataframe["entry_fill_long"] = fill_long dataframe["entry_fill_short"] = fill_short # ── LAW 4: retest bar quality ───────────────────────────────────────── candle_range = (dataframe["high"] - dataframe["low"]).clip(lower=1e-9) body = dataframe["close"] - dataframe["open"] dataframe["body_ratio"] = body.abs() / candle_range # Lower wick for LONG: distance from open to low as % of range dataframe["lower_wick"] = (dataframe[["open", "close"]].min(axis=1) - dataframe["low"]) / candle_range # Upper wick for SHORT dataframe["upper_wick"] = (dataframe["high"] - dataframe[["open", "close"]].max(axis=1)) / candle_range return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: min_br = float(self.min_body_ratio.value) min_lw = float(self.min_lower_wick.value) band = float(self.ema50_band_pct.value) / 100.0 r1_lmax = int(self.rsi_1m_long_max.value) r1_smin = int(self.rsi_1m_short_min.value) has_1m = dataframe["has_1m"] == 1 # LAW 4: conviction body + wick body_ok_long = (dataframe["body_ratio"] >= min_br) & (dataframe["lower_wick"] >= min_lw) body_ok_short = (dataframe["body_ratio"] >= min_br) & (dataframe["upper_wick"] >= min_lw) # LAW 5: 15m EMA50 structure # LONG: 15m close within band% below EMA50 (or above) → still in uptrend structure # Using band allows entries even when dip briefly dips below EMA50 ema50 = dataframe["ema50_15m"] align_long = dataframe["close"] >= ema50 * (1.0 - band) align_short = dataframe["close"] <= ema50 * (1.0 + band) # LAW 6: 1m micro (bypassed when data not available) micro_long = (~has_1m) | (dataframe["rsi_1m"] < r1_lmax) micro_short = (~has_1m) | (dataframe["rsi_1m"] > r1_smin) long_cond = ( (dataframe["entry_fill_long"] == 1) & body_ok_long & align_long & micro_long ) short_cond = ( (dataframe["entry_fill_short"] == 1) & body_ok_short & align_short & micro_short ) dataframe.loc[long_cond, "enter_long"] = 1 dataframe.loc[short_cond, "enter_short"] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe def confirm_trade_entry(self, pair, order_type, amount, rate, time_in_force, current_time, entry_tag, side, **kwargs) -> bool: self._update_loss_guard(pair, current_time) guard_until = self._guard_until.get(pair) if guard_until and current_time < guard_until: return False last = self._last_entry_time.get(pair) if last is not None: if (current_time - last).total_seconds() < self.cooldown_min.value * 60: return False self._last_entry_time[pair] = current_time return True def custom_stoploss(self, pair, trade: Trade, current_time, current_rate: float, current_profit: float, **kwargs) -> float: # Breakeven guard law: if trade moved in favor, never let it go full SL again. be_trigger = float(self.be_trigger_pct.value) / 100.0 be_lock = float(self.be_lock_pct.value) / 100.0 if current_profit >= be_trigger: is_short = getattr(trade, "is_short", False) return stoploss_from_open(be_lock, current_profit, is_short=is_short) return self.stoploss def custom_exit(self, pair, trade, current_time, current_rate, current_profit, **kwargs): if current_profit >= self.tp_pct.value / 100: return "xrsi_tp" if trade.open_date_utc: elapsed_min = (current_time - trade.open_date_utc).total_seconds() / 60 if elapsed_min >= self.max_hold_candles.value * 5: return "xrsi_timeout" return None def leverage(self, pair, current_time, current_rate, proposed_leverage, max_leverage, entry_tag, side, **kwargs) -> float: return 1.0 def _update_loss_guard(self, pair: str, current_time: datetime) -> None: # Guard law: pause new entries after a loss streak. try: closed = Trade.get_trades_proxy(pair=pair, is_open=False) except Exception: return if not closed: return closed_sorted = sorted( [t for t in closed if getattr(t, "close_date_utc", None) is not None], key=lambda t: t.close_date_utc, ) if not closed_sorted: return last_seen = self._last_closed_trade_id.get(pair, -1) streak = self._loss_streak.get(pair, 0) for t in closed_sorted: tid = getattr(t, "id", None) if tid is None or tid <= last_seen: continue profit_ratio = float(getattr(t, "close_profit", 0.0) or 0.0) if profit_ratio < 0: streak += 1 else: streak = 0 if streak >= int(self.loss_streak_limit.value): self._guard_until[pair] = t.close_date_utc + pd.Timedelta(minutes=int(self.guard_minutes.value)) streak = 0 last_seen = max(last_seen, tid) self._last_closed_trade_id[pair] = last_seen self._loss_streak[pair] = streak