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chore: use aligned quoting strategy for templtae

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This commit is contained in:
Matthias 2024-08-17 16:43:46 +02:00
parent 0995164110
commit 9408e858cd
9 changed files with 102 additions and 102 deletions
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10
freqtrade/templates/base_strategy.py.j2
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@ -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[

6
freqtrade/templates/strategy_subtemplates/buy_trend_full.j2
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@ -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

2
freqtrade/templates/strategy_subtemplates/buy_trend_minimal.j2
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@ -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

142
freqtrade/templates/strategy_subtemplates/indicators_full.j2
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@ -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]
"""

10
freqtrade/templates/strategy_subtemplates/indicators_minimal.j2
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@ -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]
"""

14
freqtrade/templates/strategy_subtemplates/plot_config_full.j2
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@ -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"},
}
}
}

6
freqtrade/templates/strategy_subtemplates/sell_trend_full.j2
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@ -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

2
freqtrade/templates/strategy_subtemplates/sell_trend_minimal.j2
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@ -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

12
freqtrade/templates/strategy_subtemplates/strategy_attributes_full.j2
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@ -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"
}