# source: https://raw.githubusercontent.com/EJMC95/crypto-trading-bot/02ae8421867e1e62555167b55d643f34165cd644/user_data/strategies/SwingDipV1.py # directory_url: https://github.com/EJMC95/crypto-trading-bot/blob/main/user_data/strategies/ # User: EJMC95 # Repository: crypto-trading-bot # --------------------# Github_EJMC95_crypto_trading_bot__SwingDipV1__20260911_153029.py # # WHAT IT IS: a SWING trader. Daily candles, holds days-to-weeks (not minutes). # It buys oversold DIPS but ONLY while the longer trend is up, and sells into # strength. The opposite rhythm to your other two bots: # v4core = slow trend rider (holds for months) # v5gated = intraday day-trader (holds minutes-to-hours) # v6swing = this one: buy-the-dip swing trades (holds ~1-2 weeks) # # THE RULE THAT KEEPS IT SANE: only buy dips when the 50-day EMA is above the # 200-day EMA. Your old V2 "bought dips" in a DOWNtrend and got destroyed # (catching falling knives). This refuses to buy a dip unless the macro trend # is still up — dip-buying with the tide, not against it. # # EVIDENCE (Binance daily, 2023-07 -> 2026-06, 0.1% fee, un-optimised settings): # 3yr return maxDD trades win # BTC buy & hold .... +120.6% -51.2% — — # BTC Github_EJMC95_crypto_trading_bot__SwingDipV1__20260911_153029 .... -5.1% -21.8% 9 44% # ETH buy & hold .... -8.8% -67.5% — — # ETH Github_EJMC95_crypto_trading_bot__SwingDipV1__20260911_153029 .... +18.1% -17.9% 7 57% # # HONEST LIMITATIONS — READ: # - It UNDERPERFORMS buy & hold in a strong bull (it sells into strength and # gives up the big trend). Its job is LOW DRAWDOWN + making money on flat/ # choppy markets (see ETH), NOT beating a raging bull. It is a diversifier. # - Small sample: ~7-13 trades over 3 years = low statistical confidence. # Treat live use as a measured dry-run experiment. # - Spot / long-only. Settings are round numbers chosen to AVOID overfitting, # not tuned for max backtest return. Hyperopt them later if you want, but # beware the V3 lesson (a great backtest is easy to fake). # # [2026-07-01] Switched entry/exit to a 20-candle RANGE strategy: buy near the # rolling 20-candle low (bottom 15% of the band), sell near the rolling 20-candle # high (top 15%). Long-only; stop-loss/ROI/protections kept as guardrails. # # [2026-07-03 ADAPTIVE DUAL-MODE] The 07-01 rewrite silently DROPPED the 50/200 # uptrend gate this bot was validated with — it was buying every 20d-low approach # in a bear market (the exact V2 knife-catch failure the docstring warns about). # Restored the gate for normal dip-buys, and added an explicit half-stake # capitulation-bounce mode (daily RSI<30 at the range bottom, up-close confirmed) # so the bot still trades the bear regime — deliberately, at reduced size, with # fast exits — instead of either churning or sitting dead. from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy import IStrategy, IntParameter class Github_EJMC95_crypto_trading_bot__SwingDipV1__20260911_153029(IStrategy): INTERFACE_VERSION = 3 timeframe = "1d" # daily candles -> swing, not scalping can_short = False # Circuit breakers (research-driven risk guards). Candle counts scale with # this strategy's timeframe. Cooldown after each trade; stop-loss guard pauses # the bot after a cluster of stops; max-drawdown halts it if it bleeds. @property def protections(self): return [ {"method": "CooldownPeriod", "stop_duration_candles": 1}, {"method": "StoplossGuard", "lookback_period_candles": 20, "trade_limit": 2, "stop_duration_candles": 5, "only_per_pair": False}, {"method": "MaxDrawdown", "lookback_period_candles": 40, "trade_limit": 4, "stop_duration_candles": 5, "max_allowed_drawdown": 0.2}, ] # Hyperopt-tunable, but defaults are deliberately round (anti-overfit). buy_rsi = IntParameter(20, 55, default=42, space="buy", optimize=False) sell_rsi = IntParameter(55, 80, default=65, space="sell", optimize=False) # Take-profit ladder in MINUTES (1 day = 1440). Read in days: # grab +20% any time; after 4 days accept +12%; after 8 days +6%; # after 14 days exit at break-even. -> natural ~2-week max swing. minimal_roi = { "0": 0.20, "5760": 0.12, "11520": 0.06, "20160": 0.0, } # Stop: daily swings need room, and the uptrend filter limits downside. # [2026-07-03] -0.15 -> -0.10. Backtest 2024-01->2025-12 (15-pair basket): # 58% win rate yet -29% net — winners exit at the range top (~+5-8%) while # losers rode all the way to -15%, an unbeatable ratio. -10% keeps the # asymmetry survivable without clipping ordinary daily noise. stoploss = -0.10 trailing_stop = False use_exit_signal = True # the "sell into strength" exit below exit_profit_only = False process_only_new_candles = True startup_candle_count = 200 # need 200 daily candles for the 200d EMA def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) bb = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe["bb_lower"] = bb["lower"] dataframe["bb_upper"] = bb["upper"] # [20-CANDLE RANGE 2026-07-01] Adaptive support/resistance: buy near the # rolling 20-candle low, sell near the rolling 20-candle high. shift(1) keeps # the current forming bar out of its own band (no look-ahead). dataframe["rng_low20"] = dataframe["low"].rolling(20).min().shift(1) dataframe["rng_high20"] = dataframe["high"].rolling(20).max().shift(1) _rng_band = (dataframe["rng_high20"] - dataframe["rng_low20"]).clip(lower=1e-9) # [2026-07-03 ADAPTIVE] Buy zone widened 0.15 -> 0.20 (more dip entries in # the friendly regime). dataframe["rng_buy_zone"] = dataframe["rng_low20"] + 0.20 * _rng_band dataframe["rng_sell_zone"] = dataframe["rng_high20"] - 0.15 * _rng_band return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: live_vol = dataframe["volume"] > 0 # UPTREND: the ORIGINAL VALIDATED entry — a genuine oversold dip (RSI<35 # AND close under the lower Bollinger) inside an uptrend. [2026-07-03] # Backtests showed the 07-01 "buy near the 20d range low" replacement lost # -29% even in the 2024-25 up-window; the Bollinger dip demands real # capitulation, not every drift to the bottom of a drifting range. dataframe.loc[ ( (dataframe["ema50"] > dataframe["ema200"]) & (dataframe["rsi"] < self.buy_rsi.value) & (dataframe["close"] < dataframe["bb_lower"]) & live_vol ), ["enter_long", "enter_tag"], ] = (1, "dip_in_uptrend") # [2026-07-12 SLEEVE RETIRED] The bear_bounce leg (downtrend half-stake # capitulation bounce, shipped 07-03 to all four spot bots) is gone # fleet-wide: tagged Binance replay 2022-2026 scored it negative in ALL # FOUR carriers (19 entries, -$7.27 aggregate, 26% win) and it never # fired once in live paper. In a downtrend this bot stands down — the # originally validated behaviour. return dataframe # [2026-07-04 PULSE] Panic-cluster check (news shocks: hacks/SEC/depegs). # Mirrors DayTraderV5Gated: sizing only, fail-safe neutral, 15-min cache. _pulse_cache = {"ts": None, "panic": False} def _pulse_panic(self, current_time): c = type(self)._pulse_cache try: if c["ts"] is not None and (current_time - c["ts"]).total_seconds() < 900: return c["panic"] import bot_pnl_store as store st = store.load_state("market-pulse") or {} latest = st.get("latest") or {} # [2026-07-17 IMB-04] gate on the payload's OWN updated+ttl_sec — # a fossil panic=true from a dead market_pulse must not halve # stakes forever. Stale/unstamped/future-dated -> False. from datetime import datetime as _dt, timezone as _tz _age = (_dt.now(_tz.utc) - _dt.fromisoformat( str(st.get("updated")).replace("Z", "+00:00"))).total_seconds() c["ts"], c["panic"] = current_time, ( bool(latest.get("panic")) and 0 <= _age <= float(st.get("ttl_sec") or 0)) except Exception: c["ts"], c["panic"] = current_time, False return c["panic"] # [2026-07-04] During an active PANIC news cluster all new stakes halve: # dip-buying into a live hack/regulatory shock is the knife-catch case. def custom_stake_amount(self, pair, current_time, current_rate, proposed_stake, min_stake, max_stake, leverage, entry_tag, side, **kwargs): stake = proposed_stake if self._pulse_panic(current_time): stake *= 0.5 # [2026-07-14 L4] Brain's reduce-only per-tag multiplier — neutral 1.0 # on any doubt (fleet_bus fail-safe contract). try: import fleet_bus stake *= fleet_bus.stake_multiplier( self.config.get("bot_name"), entry_tag, current_time) except Exception: pass if stake < proposed_stake and min_stake is not None and stake < min_stake: stake = min_stake return stake def confirm_trade_entry(self, pair, order_type, amount, rate, time_in_force, current_time, entry_tag, side, **kwargs): # [2026-07-14 L2] Fleet-risk long-budget veto (26-position-pileup guard, # per the 07-07 design's Jul-14 enforcement review). Fail-safe OPEN. try: import fleet_bus if fleet_bus.long_entries_blocked(current_time): return False except Exception: pass return True def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # [2026-07-03] Sell into strength, two ways: the validated RSI>65 exit OR # price reaching the top of the 20d range — whichever comes first. More # closes, and winners are banked into rallies instead of round-tripping. dataframe.loc[ ( ( (dataframe["rsi"] > self.sell_rsi.value) | (dataframe["close"] >= dataframe["rng_sell_zone"]) ) & (dataframe["volume"] > 0) ), "exit_long", ] = 1 return dataframe