PocketPine/test_strategy.py at main · ChipaDevTeam/PocketPine · GitHub

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"""Auto-generated by PocketPine transpiler."""

import numpy as np
from pocketpine import indicators as ta
from pocketpine.strategy import Strategy
from pocketpine.signal_parser import Signal, Action


class PineStrategy(Strategy):

    def setup(self):
        self.p = {
            'patternStrength': 3,
            'bodyRatio': 0.65,
            'shadowRatio': 2.2,
            'patternLookback': 3,
            'minPatternSize': 0.5,
            'enableHammer': True,
            'enableDoji': True,
            'enableEngulfing': True,
            'enableStar': True,
            'enableHarami': True,
            'enablePiercing': True,
            'enableShootingStar': True,
            'enableInvertedHammer': True,
            'useVolumeConfirmation': True,
            'volumeMultiplier': 1.15,
            'useTrendConfirmation': True,
            'trendPeriod': 20,
            'useRSIConfirmation': True,
            'rsiPeriod': 14,
            'rsiOverbought': 70,
            'rsiOversold': 30,
            'useMAConfirmation': False,
            'maFastPeriod': 9,
            'maSlowPeriod': 21,
            'stopLossType': "ATR",
            'stopLossPercent': 1.5,
            'atrPeriod': 14,
            'atrMultiplier': 1.6,
            'takeProfitRatio': 3.2,
            'showTPSL': True,
            'showLabels': True,
            'maxSignalsDisplay': 10,
        }

        self._lastSignalType = None

    def _calculateStopLoss(self, isLong, entryPrice, **kw):
        p = self.p
        close = kw.get('close', self.bars.close)
        open_ = kw.get('open_', self.bars.open)
        high = kw.get('high', self.bars.high)
        low = kw.get('low', self.bars.low)
        volume = kw.get('volume', self.bars.volume)
        if p['stopLossType'] == "Percentage":
            if isLong:
                return entryPrice * (1 - p['stopLossPercent'] / 100)
            else:
                return entryPrice * (1 + p['stopLossPercent'] / 100)
        elif p['stopLossType'] == "ATR":
            if isLong:
                return entryPrice - atrValue * p['atrMultiplier']
            else:
                return entryPrice + atrValue * p['atrMultiplier']
        else:
            if isLong:
                return np.minimum(low, ta.shift(low, 1)) - atrValue * 0.5
            else:
                return np.maximum(high, ta.shift(high, 1)) + atrValue * 0.5

    def _calculateTakeProfit(self, isLong, entryPrice, stopLoss, **kw):
        p = self.p
        close = kw.get('close', self.bars.close)
        open_ = kw.get('open_', self.bars.open)
        high = kw.get('high', self.bars.high)
        low = kw.get('low', self.bars.low)
        volume = kw.get('volume', self.bars.volume)
        stopDistance = np.abs(entryPrice - stopLoss)
        if isLong:
            return entryPrice + stopDistance * p['takeProfitRatio']
        else:
            return entryPrice - stopDistance * p['takeProfitRatio']

    def compute(self):
        p = self.p
        close = self.bars.close
        open_ = self.bars.open
        high = self.bars.high
        low = self.bars.low
        volume = self.bars.volume
        if len(close) < 3:
            return

        atrValue = ta.atr(high, low, close, p['atrPeriod'])
        ema = ta.ema(close, p['trendPeriod'])
        uptrend = close > ema
        downtrend = close < ema
        maFast = ta.ema(close, p['maFastPeriod'])
        maSlow = ta.ema(close, p['maSlowPeriod'])
        bullishTrendMA = ((maFast > maSlow) & (close > maFast))
        bearishTrendMA = ((maFast < maSlow) & (close < maFast))
        rsi = ta.rsi(close, p['rsiPeriod'])
        rsiOverboughtCondition = rsi > p['rsiOverbought']
        rsiOversoldCondition = rsi < p['rsiOversold']
        avgVolume = ta.sma(volume, 20)
        highVolume = volume > avgVolume * p['volumeMultiplier']
        bodySize = np.abs(close - open_)
        totalRange = high - low
        upperShadow = high - np.maximum(close, open_)
        lowerShadow = np.minimum(close, open_) - low
        bodyRatioActual = np.where(totalRange > 0, bodySize / totalRange, 0)
        upperShadowRatio = np.where(bodySize > 0, upperShadow / bodySize, 0)
        lowerShadowRatio = np.where(bodySize > 0, lowerShadow / bodySize, 0)
        bullishCandle = close > open_
        bearishCandle = close < open_
        bullishDoji = (p['enableDoji'] & (((bodyRatioActual < 0.1) & (totalRange > atrValue * 0.5))) & downtrend)
        bullishHammer = (p['enableHammer'] & (((lowerShadow >= bodySize * p['shadowRatio']) & (upperShadow <= bodySize * 0.5) & (bodyRatioActual >= p['bodyRatio'] * 0.5) & (bodyRatioActual <= 0.8))) & downtrend)
        bullishInvertedHammer = (p['enableInvertedHammer'] & (((upperShadow >= bodySize * p['shadowRatio']) & (lowerShadow <= bodySize * 0.5) & (bodyRatioActual >= p['bodyRatio'] * 0.5))) & downtrend)
        bullishEngulfing = (p['enableEngulfing'] & ((ta.shift(bearishCandle, 1) & bullishCandle & (open_ <= ta.shift(close, 1)) & (close > ta.shift(open_, 1)) & (bodySize > ta.shift(bodySize, 1) * 1.2))))
        bullishMorningStar = (p['enableStar'] & ((ta.shift(bearishCandle, 2) & (ta.shift(bodySize, 2) > atrValue) & (ta.shift(bodyRatioActual, 1) < 0.3) & bullishCandle & (close > (ta.shift(open_, 2) + ta.shift(close, 2)) / 2))))
        bullishHarami = (p['enableHarami'] & ((ta.shift(bearishCandle, 1) & bullishCandle & (open_ > ta.shift(close, 1)) & (close < ta.shift(open_, 1)) & (bodySize < ta.shift(bodySize, 1) * 0.7))))
        bullishPiercing = (p['enablePiercing'] & ((ta.shift(bearishCandle, 1) & bullishCandle & (open_ < ta.shift(low, 1)) & (close > (ta.shift(open_, 1) + ta.shift(close, 1)) / 2) & (close < ta.shift(open_, 1)))))
        bearishDoji = (p['enableDoji'] & (((bodyRatioActual < 0.1) & (totalRange > atrValue * 0.5))) & uptrend)
        bearishShootingStar = (p['enableShootingStar'] & (((upperShadow >= bodySize * p['shadowRatio']) & (lowerShadow <= bodySize * 0.5) & (bodyRatioActual >= p['bodyRatio'] * 0.5) & (bodyRatioActual <= 0.8))) & uptrend)
        bearishHangingMan = (p['enableHammer'] & (((lowerShadow >= bodySize * p['shadowRatio']) & (upperShadow <= bodySize * 0.5) & (bodyRatioActual >= p['bodyRatio'] * 0.5) & (bodyRatioActual <= 0.8))) & uptrend)
        bearishEngulfing = (p['enableEngulfing'] & ((ta.shift(bullishCandle, 1) & bearishCandle & (open_ >= ta.shift(close, 1)) & (close < ta.shift(open_, 1)) & (bodySize > ta.shift(bodySize, 1) * 1.2))))
        bearishEveningStar = (p['enableStar'] & ((ta.shift(bullishCandle, 2) & (ta.shift(bodySize, 2) > atrValue) & (ta.shift(bodyRatioActual, 1) < 0.3) & bearishCandle & (close < (ta.shift(open_, 2) + ta.shift(close, 2)) / 2))))
        bearishHarami = (p['enableHarami'] & ((ta.shift(bullishCandle, 1) & bearishCandle & (open_ < ta.shift(close, 1)) & (close > ta.shift(open_, 1)) & (bodySize < ta.shift(bodySize, 1) * 0.7))))
        bearishDarkCloud = (p['enablePiercing'] & ((ta.shift(bullishCandle, 1) & bearishCandle & (open_ > ta.shift(high, 1)) & (close < (ta.shift(open_, 1) + ta.shift(close, 1)) / 2) & (close > ta.shift(open_, 1)))))
        bullishPatternDetected = (bullishDoji) | (bullishHammer) | (bullishInvertedHammer) | (bullishEngulfing) | (bullishMorningStar) | (bullishHarami) | (bullishPiercing)
        bearishPatternDetected = (bearishDoji) | (bearishShootingStar) | (bearishHangingMan) | (bearishEngulfing) | (bearishEveningStar) | (bearishHarami) | (bearishDarkCloud)
        volumeConfirmed = np.where(p['useVolumeConfirmation'], highVolume, True)
        trendConfirmedBullish = np.where(p['useTrendConfirmation'], ((close > ema * 0.99)) | (~uptrend), True)
        trendConfirmedBearish = np.where(p['useTrendConfirmation'], ((close < ema * 1.01)) | (~downtrend), True)
        maConfirmedBullish = np.where(p['useMAConfirmation'], bullishTrendMA, True)
        maConfirmedBearish = np.where(p['useMAConfirmation'], bearishTrendMA, True)
        rsiConfirmedBullish = np.where(p['useRSIConfirmation'], ((rsi < 55) & ~rsiOverboughtCondition), True)
        rsiConfirmedBearish = np.where(p['useRSIConfirmation'], ((rsi > 45) & ~rsiOversoldCondition), True)
        buySignalRaw = (ta.shift(bullishPatternDetected, 1) & ta.shift(volumeConfirmed, 1) & ta.shift(trendConfirmedBullish, 1) & ta.shift(rsiConfirmedBullish, 1) & ta.shift(maConfirmedBullish, 1))
        sellSignalRaw = (ta.shift(bearishPatternDetected, 1) & ta.shift(volumeConfirmed, 1) & ta.shift(trendConfirmedBearish, 1) & ta.shift(rsiConfirmedBearish, 1) & ta.shift(maConfirmedBearish, 1))
        buySignal = ((buySignalRaw & (self._lastSignalType != "BUY"))) | ((self._lastSignalType is None))
        sellSignal = ((sellSignalRaw & (self._lastSignalType != "SELL"))) | ((self._lastSignalType is None))

        # Store signals
        self._sellSignalRaw = sellSignalRaw if isinstance(sellSignalRaw, np.ndarray) else np.full(1, bool(sellSignalRaw))
        self._buySignal = buySignal if isinstance(buySignal, np.ndarray) else np.full(1, bool(buySignal))
        self._sellSignal = sellSignal if isinstance(sellSignal, np.ndarray) else np.full(1, bool(sellSignal))
        self._bearishPatternDetected = bearishPatternDetected if isinstance(bearishPatternDetected, np.ndarray) else np.full(1, bool(bearishPatternDetected))
        self._buySignalRaw = buySignalRaw if isinstance(buySignalRaw, np.ndarray) else np.full(1, bool(buySignalRaw))
        self._bullishPatternDetected = bullishPatternDetected if isinstance(bullishPatternDetected, np.ndarray) else np.full(1, bool(bullishPatternDetected))

    def get_signal(self):
        if len(self.bars) < 3:
            return None
        try:
            if bool(self._buySignal[-1]) and self._lastSignalType != 'BUY':
                self._lastSignalType = 'BUY'
                return Signal(Action.BUY, self.asset, self.amount, self.duration)
        except (IndexError, TypeError): pass
        try:
            if bool(self._sellSignal[-1]) and self._lastSignalType != 'SELL':
                self._lastSignalType = 'SELL'
                return Signal(Action.SELL, self.asset, self.amount, self.duration)
        except (IndexError, TypeError): pass
        return None