From 9408e858cd4c5ba54fcffb35907695a0224f87c7 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 17 Aug 2024 16:43:46 +0200 Subject: [PATCH] chore: use aligned quoting strategy for templtae --- freqtrade/templates/base_strategy.py.j2 | 10 +- .../strategy_subtemplates/buy_trend_full.j2 | 6 +- .../buy_trend_minimal.j2 | 2 +- .../strategy_subtemplates/indicators_full.j2 | 142 +++++++++--------- .../indicators_minimal.j2 | 10 +- .../strategy_subtemplates/plot_config_full.j2 | 14 +- .../strategy_subtemplates/sell_trend_full.j2 | 6 +- .../sell_trend_minimal.j2 | 2 +- .../strategy_attributes_full.j2 | 12 +- 9 files changed, 102 insertions(+), 102 deletions(-) diff --git a/freqtrade/templates/base_strategy.py.j2 b/freqtrade/templates/base_strategy.py.j2 index 2e8250dd2..a61093ebd 100644 --- a/freqtrade/templates/base_strategy.py.j2 +++ b/freqtrade/templates/base_strategy.py.j2 @@ -55,7 +55,7 @@ class {{ strategy }}(IStrategy): INTERFACE_VERSION = 3 # Optimal timeframe for the strategy. - timeframe = '5m' + timeframe = "5m" # Can this strategy go short? can_short: bool = False @@ -134,9 +134,9 @@ class {{ strategy }}(IStrategy): dataframe.loc[ ( {{ buy_trend | indent(16) }} - (dataframe['volume'] > 0) # Make sure Volume is not 0 + (dataframe["volume"] > 0) # Make sure Volume is not 0 ), - 'enter_long'] = 1 + "enter_long"] = 1 # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info) """ dataframe.loc[ @@ -159,9 +159,9 @@ class {{ strategy }}(IStrategy): dataframe.loc[ ( {{ sell_trend | indent(16) }} - (dataframe['volume'] > 0) # Make sure Volume is not 0 + (dataframe["volume"] > 0) # Make sure Volume is not 0 ), - 'exit_long'] = 1 + "exit_long"] = 1 # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info) """ dataframe.loc[ diff --git a/freqtrade/templates/strategy_subtemplates/buy_trend_full.j2 b/freqtrade/templates/strategy_subtemplates/buy_trend_full.j2 index aac8325a7..7a50fd4dc 100644 --- a/freqtrade/templates/strategy_subtemplates/buy_trend_full.j2 +++ b/freqtrade/templates/strategy_subtemplates/buy_trend_full.j2 @@ -1,3 +1,3 @@ -(qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) & # Signal: RSI crosses above buy_rsi -(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle -(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising +(qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)) & # Signal: RSI crosses above buy_rsi +(dataframe["tema"] <= dataframe["bb_middleband"]) & # Guard: tema below BB middle +(dataframe["tema"] > dataframe["tema"].shift(1)) & # Guard: tema is raising diff --git a/freqtrade/templates/strategy_subtemplates/buy_trend_minimal.j2 b/freqtrade/templates/strategy_subtemplates/buy_trend_minimal.j2 index e89d3779e..bcecacc3c 100644 --- a/freqtrade/templates/strategy_subtemplates/buy_trend_minimal.j2 +++ b/freqtrade/templates/strategy_subtemplates/buy_trend_minimal.j2 @@ -1 +1 @@ -(qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) & # Signal: RSI crosses above buy_rsi +(qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)) & # Signal: RSI crosses above buy_rsi diff --git a/freqtrade/templates/strategy_subtemplates/indicators_full.j2 b/freqtrade/templates/strategy_subtemplates/indicators_full.j2 index a497b47cb..e4c4daac4 100644 --- a/freqtrade/templates/strategy_subtemplates/indicators_full.j2 +++ b/freqtrade/templates/strategy_subtemplates/indicators_full.j2 @@ -3,24 +3,24 @@ # ------------------------------------ # ADX -dataframe['adx'] = ta.ADX(dataframe) +dataframe["adx"] = ta.ADX(dataframe) # # Plus Directional Indicator / Movement -# dataframe['plus_dm'] = ta.PLUS_DM(dataframe) -# dataframe['plus_di'] = ta.PLUS_DI(dataframe) +# dataframe["plus_dm"] = ta.PLUS_DM(dataframe) +# dataframe["plus_di"] = ta.PLUS_DI(dataframe) # # Minus Directional Indicator / Movement -# dataframe['minus_dm'] = ta.MINUS_DM(dataframe) -# dataframe['minus_di'] = ta.MINUS_DI(dataframe) +# dataframe["minus_dm"] = ta.MINUS_DM(dataframe) +# dataframe["minus_di"] = ta.MINUS_DI(dataframe) # # Aroon, Aroon Oscillator # aroon = ta.AROON(dataframe) -# dataframe['aroonup'] = aroon['aroonup'] -# dataframe['aroondown'] = aroon['aroondown'] -# dataframe['aroonosc'] = ta.AROONOSC(dataframe) +# dataframe["aroonup"] = aroon["aroonup"] +# dataframe["aroondown"] = aroon["aroondown"] +# dataframe["aroonosc"] = ta.AROONOSC(dataframe) # # Awesome Oscillator -# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) +# dataframe["ao"] = qtpylib.awesome_oscillator(dataframe) # # Keltner Channel # keltner = qtpylib.keltner_channel(dataframe) @@ -36,58 +36,58 @@ dataframe['adx'] = ta.ADX(dataframe) # ) # # Ultimate Oscillator -# dataframe['uo'] = ta.ULTOSC(dataframe) +# dataframe["uo"] = ta.ULTOSC(dataframe) # # Commodity Channel Index: values [Oversold:-100, Overbought:100] -# dataframe['cci'] = ta.CCI(dataframe) +# dataframe["cci"] = ta.CCI(dataframe) # RSI -dataframe['rsi'] = ta.RSI(dataframe) +dataframe["rsi"] = ta.RSI(dataframe) # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy) -# rsi = 0.1 * (dataframe['rsi'] - 50) -# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) +# rsi = 0.1 * (dataframe["rsi"] - 50) +# dataframe["fisher_rsi"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy) -# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) +# dataframe["fisher_rsi_norma"] = 50 * (dataframe["fisher_rsi"] + 1) # # Stochastic Slow # stoch = ta.STOCH(dataframe) -# dataframe['slowd'] = stoch['slowd'] -# dataframe['slowk'] = stoch['slowk'] +# dataframe["slowd"] = stoch["slowd"] +# dataframe["slowk"] = stoch["slowk"] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) -dataframe['fastd'] = stoch_fast['fastd'] -dataframe['fastk'] = stoch_fast['fastk'] +dataframe["fastd"] = stoch_fast["fastd"] +dataframe["fastk"] = stoch_fast["fastk"] # # Stochastic RSI # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this. # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results. # stoch_rsi = ta.STOCHRSI(dataframe) -# dataframe['fastd_rsi'] = stoch_rsi['fastd'] -# dataframe['fastk_rsi'] = stoch_rsi['fastk'] +# dataframe["fastd_rsi"] = stoch_rsi["fastd"] +# dataframe["fastk_rsi"] = stoch_rsi["fastk"] # MACD macd = ta.MACD(dataframe) -dataframe['macd'] = macd['macd'] -dataframe['macdsignal'] = macd['macdsignal'] -dataframe['macdhist'] = macd['macdhist'] +dataframe["macd"] = macd["macd"] +dataframe["macdsignal"] = macd["macdsignal"] +dataframe["macdhist"] = macd["macdhist"] # MFI -dataframe['mfi'] = ta.MFI(dataframe) +dataframe["mfi"] = ta.MFI(dataframe) # # ROC -# dataframe['roc'] = ta.ROC(dataframe) +# dataframe["roc"] = ta.ROC(dataframe) # Overlap Studies # ------------------------------------ # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) -dataframe['bb_lowerband'] = bollinger['lower'] -dataframe['bb_middleband'] = bollinger['mid'] -dataframe['bb_upperband'] = bollinger['upper'] +dataframe["bb_lowerband"] = bollinger["lower"] +dataframe["bb_middleband"] = bollinger["mid"] +dataframe["bb_upperband"] = bollinger["upper"] dataframe["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) @@ -112,95 +112,95 @@ dataframe["bb_width"] = ( # ) # # EMA - Exponential Moving Average -# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) -# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) -# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) -# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) -# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) -# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) +# dataframe["ema3"] = ta.EMA(dataframe, timeperiod=3) +# dataframe["ema5"] = ta.EMA(dataframe, timeperiod=5) +# dataframe["ema10"] = ta.EMA(dataframe, timeperiod=10) +# dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21) +# dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50) +# dataframe["ema100"] = ta.EMA(dataframe, timeperiod=100) # # SMA - Simple Moving Average -# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3) -# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) -# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10) -# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21) -# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) -# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) +# dataframe["sma3"] = ta.SMA(dataframe, timeperiod=3) +# dataframe["sma5"] = ta.SMA(dataframe, timeperiod=5) +# dataframe["sma10"] = ta.SMA(dataframe, timeperiod=10) +# dataframe["sma21"] = ta.SMA(dataframe, timeperiod=21) +# dataframe["sma50"] = ta.SMA(dataframe, timeperiod=50) +# dataframe["sma100"] = ta.SMA(dataframe, timeperiod=100) # Parabolic SAR -dataframe['sar'] = ta.SAR(dataframe) +dataframe["sar"] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average -dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) +dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) # Cycle Indicator # ------------------------------------ # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) -dataframe['htsine'] = hilbert['sine'] -dataframe['htleadsine'] = hilbert['leadsine'] +dataframe["htsine"] = hilbert["sine"] +dataframe["htleadsine"] = hilbert["leadsine"] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ # # Hammer: values [0, 100] -# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) +# dataframe["CDLHAMMER"] = ta.CDLHAMMER(dataframe) # # Inverted Hammer: values [0, 100] -# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) +# dataframe["CDLINVERTEDHAMMER"] = ta.CDLINVERTEDHAMMER(dataframe) # # Dragonfly Doji: values [0, 100] -# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) +# dataframe["CDLDRAGONFLYDOJI"] = ta.CDLDRAGONFLYDOJI(dataframe) # # Piercing Line: values [0, 100] -# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] +# dataframe["CDLPIERCING"] = ta.CDLPIERCING(dataframe) # values [0, 100] # # Morningstar: values [0, 100] -# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] +# dataframe["CDLMORNINGSTAR"] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # # Three White Soldiers: values [0, 100] -# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] +# dataframe["CDL3WHITESOLDIERS"] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ # # Hanging Man: values [0, 100] -# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) +# dataframe["CDLHANGINGMAN"] = ta.CDLHANGINGMAN(dataframe) # # Shooting Star: values [0, 100] -# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) +# dataframe["CDLSHOOTINGSTAR"] = ta.CDLSHOOTINGSTAR(dataframe) # # Gravestone Doji: values [0, 100] -# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) +# dataframe["CDLGRAVESTONEDOJI"] = ta.CDLGRAVESTONEDOJI(dataframe) # # Dark Cloud Cover: values [0, 100] -# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) +# dataframe["CDLDARKCLOUDCOVER"] = ta.CDLDARKCLOUDCOVER(dataframe) # # Evening Doji Star: values [0, 100] -# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) +# dataframe["CDLEVENINGDOJISTAR"] = ta.CDLEVENINGDOJISTAR(dataframe) # # Evening Star: values [0, 100] -# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) +# dataframe["CDLEVENINGSTAR"] = ta.CDLEVENINGSTAR(dataframe) # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ # # Three Line Strike: values [0, -100, 100] -# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) +# dataframe["CDL3LINESTRIKE"] = ta.CDL3LINESTRIKE(dataframe) # # Spinning Top: values [0, -100, 100] -# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] +# dataframe["CDLSPINNINGTOP"] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # # Engulfing: values [0, -100, 100] -# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] +# dataframe["CDLENGULFING"] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # # Harami: values [0, -100, 100] -# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] +# dataframe["CDLHARAMI"] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # # Three Outside Up/Down: values [0, -100, 100] -# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] +# dataframe["CDL3OUTSIDE"] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # # Three Inside Up/Down: values [0, -100, 100] -# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] +# dataframe["CDL3INSIDE"] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] # # Chart type # # ------------------------------------ # # Heikin Ashi Strategy # heikinashi = qtpylib.heikinashi(dataframe) -# dataframe['ha_open'] = heikinashi['open'] -# dataframe['ha_close'] = heikinashi['close'] -# dataframe['ha_high'] = heikinashi['high'] -# dataframe['ha_low'] = heikinashi['low'] +# dataframe["ha_open"] = heikinashi["open"] +# dataframe["ha_close"] = heikinashi["close"] +# dataframe["ha_high"] = heikinashi["high"] +# dataframe["ha_low"] = heikinashi["low"] # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # first check if dataprovider is available if self.dp: - if self.dp.runmode.value in ('live', 'dry_run'): - ob = self.dp.orderbook(metadata['pair'], 1) - dataframe['best_bid'] = ob['bids'][0][0] - dataframe['best_ask'] = ob['asks'][0][0] + if self.dp.runmode.value in ("live", "dry_run"): + ob = self.dp.orderbook(metadata["pair"], 1) + dataframe["best_bid"] = ob["bids"][0][0] + dataframe["best_ask"] = ob["asks"][0][0] """ diff --git a/freqtrade/templates/strategy_subtemplates/indicators_minimal.j2 b/freqtrade/templates/strategy_subtemplates/indicators_minimal.j2 index 90f4f4d4a..1594a8988 100644 --- a/freqtrade/templates/strategy_subtemplates/indicators_minimal.j2 +++ b/freqtrade/templates/strategy_subtemplates/indicators_minimal.j2 @@ -3,15 +3,15 @@ # ------------------------------------ # RSI -dataframe['rsi'] = ta.RSI(dataframe) +dataframe["rsi"] = ta.RSI(dataframe) # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # first check if dataprovider is available if self.dp: - if self.dp.runmode.value in ('live', 'dry_run'): - ob = self.dp.orderbook(metadata['pair'], 1) - dataframe['best_bid'] = ob['bids'][0][0] - dataframe['best_ask'] = ob['asks'][0][0] + if self.dp.runmode.value in ("live", "dry_run"): + ob = self.dp.orderbook(metadata["pair"], 1) + dataframe["best_bid"] = ob["bids"][0][0] + dataframe["best_ask"] = ob["asks"][0][0] """ diff --git a/freqtrade/templates/strategy_subtemplates/plot_config_full.j2 b/freqtrade/templates/strategy_subtemplates/plot_config_full.j2 index e3f9e7ca0..08eb3c29f 100644 --- a/freqtrade/templates/strategy_subtemplates/plot_config_full.j2 +++ b/freqtrade/templates/strategy_subtemplates/plot_config_full.j2 @@ -3,18 +3,18 @@ def plot_config(self): return { # Main plot indicators (Moving averages, ...) - 'main_plot': { - 'tema': {}, - 'sar': {'color': 'white'}, + "main_plot": { + "tema": {}, + "sar": {"color": "white"}, }, - 'subplots': { + "subplots": { # Subplots - each dict defines one additional plot "MACD": { - 'macd': {'color': 'blue'}, - 'macdsignal': {'color': 'orange'}, + "macd": {"color": "blue"}, + "macdsignal": {"color": "orange"}, }, "RSI": { - 'rsi': {'color': 'red'}, + "rsi": {"color": "red"}, } } } diff --git a/freqtrade/templates/strategy_subtemplates/sell_trend_full.j2 b/freqtrade/templates/strategy_subtemplates/sell_trend_full.j2 index 3068d8d57..08cb68cd1 100644 --- a/freqtrade/templates/strategy_subtemplates/sell_trend_full.j2 +++ b/freqtrade/templates/strategy_subtemplates/sell_trend_full.j2 @@ -1,3 +1,3 @@ -(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) & # Signal: RSI crosses above sell_rsi -(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle -(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling +(qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value)) & # Signal: RSI crosses above sell_rsi +(dataframe["tema"] > dataframe["bb_middleband"]) & # Guard: tema above BB middle +(dataframe["tema"] < dataframe["tema"].shift(1)) & # Guard: tema is falling diff --git a/freqtrade/templates/strategy_subtemplates/sell_trend_minimal.j2 b/freqtrade/templates/strategy_subtemplates/sell_trend_minimal.j2 index 5dabc5910..821b547c3 100644 --- a/freqtrade/templates/strategy_subtemplates/sell_trend_minimal.j2 +++ b/freqtrade/templates/strategy_subtemplates/sell_trend_minimal.j2 @@ -1 +1 @@ -(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) & # Signal: RSI crosses above sell_rsi +(qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value)) & # Signal: RSI crosses above sell_rsi diff --git a/freqtrade/templates/strategy_subtemplates/strategy_attributes_full.j2 b/freqtrade/templates/strategy_subtemplates/strategy_attributes_full.j2 index 86445510d..5ae361996 100644 --- a/freqtrade/templates/strategy_subtemplates/strategy_attributes_full.j2 +++ b/freqtrade/templates/strategy_subtemplates/strategy_attributes_full.j2 @@ -1,13 +1,13 @@ # Optional order type mapping. order_types = { - 'entry': 'limit', - 'exit': 'limit', - 'stoploss': 'market', - 'stoploss_on_exchange': False + "entry": "limit", + "exit": "limit", + "stoploss": "market", + "stoploss_on_exchange": False } # Optional order time in force. order_time_in_force = { - 'entry': 'GTC', - 'exit': 'GTC' + "entry": "GTC", + "exit": "GTC" }