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Jérôme Delacotte
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custom_indicators.py
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custom_indicators.py
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"""
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Solipsis Custom Indicators and Maths
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"""
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import numpy as np
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from pandas import DataFrame, Series
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"""
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Misc. Helper Functions
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"""
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def same_length(bigger, shorter):
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return np.concatenate((np.full((bigger.shape[0] - shorter.shape[0]), np.nan), shorter))
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"""
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Maths
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"""
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def linear_growth(start: float, end: float, start_time: int, end_time: int, trade_time: int) -> float:
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"""
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Simple linear growth function. Grows from start to end after end_time minutes (starts after start_time minutes)
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"""
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time = max(0, trade_time - start_time)
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rate = (end - start) / (end_time - start_time)
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return min(end, start + (rate * time))
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def linear_decay(start: float, end: float, start_time: int, end_time: int, trade_time: int) -> float:
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"""
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Simple linear decay function. Decays from start to end after end_time minutes (starts after start_time minutes)
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"""
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time = max(0, trade_time - start_time)
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rate = (start - end) / (end_time - start_time)
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return max(end, start - (rate * time))
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"""
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TA Indicators
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"""
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def zema(dataframe, period, field='close'):
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"""
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Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/overlap_studies.py#L79
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Modified slightly to use ta.EMA instead of technical ema
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"""
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df = dataframe.copy()
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df['ema1'] = ta.EMA(df[field], timeperiod=period)
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df['ema2'] = ta.EMA(df['ema1'], timeperiod=period)
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df['d'] = df['ema1'] - df['ema2']
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df['zema'] = df['ema1'] + df['d']
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return df['zema']
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def RMI(dataframe, *, length=20, mom=5):
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"""
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Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912
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"""
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df = dataframe.copy()
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df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0)
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df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0)
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df.fillna(0, inplace=True)
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df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length)
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df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length)
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df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"]))
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return df["RMI"]
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def mastreak(dataframe: DataFrame, period: int = 4, field='close') -> Series:
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"""
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MA Streak
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Port of: https://www.tradingview.com/script/Yq1z7cIv-MA-Streak-Can-Show-When-a-Run-Is-Getting-Long-in-the-Tooth/
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"""
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df = dataframe.copy()
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avgval = zema(df, period, field)
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arr = np.diff(avgval)
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pos = np.clip(arr, 0, 1).astype(bool).cumsum()
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neg = np.clip(arr, -1, 0).astype(bool).cumsum()
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streak = np.where(arr >= 0, pos - np.maximum.accumulate(np.where(arr <= 0, pos, 0)),
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-neg + np.maximum.accumulate(np.where(arr >= 0, neg, 0)))
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res = same_length(df['close'], streak)
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return res
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def pcc(dataframe: DataFrame, period: int = 20, mult: int = 2):
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"""
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Percent Change Channel
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PCC is like KC unless it uses percentage changes in price to set channel distance.
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https://www.tradingview.com/script/6wwAWXA1-MA-Streak-Change-Channel/
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"""
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df = dataframe.copy()
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df['previous_close'] = df['close'].shift()
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df['close_change'] = (df['close'] - df['previous_close']) / df['previous_close'] * 100
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df['high_change'] = (df['high'] - df['close']) / df['close'] * 100
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df['low_change'] = (df['low'] - df['close']) / df['close'] * 100
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df['delta'] = df['high_change'] - df['low_change']
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mid = zema(df, period, 'close_change')
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rangema = zema(df, period, 'delta')
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upper = mid + rangema * mult
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lower = mid - rangema * mult
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return upper, rangema, lower
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def SSLChannels(dataframe, length=10, mode='sma'):
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"""
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Source: https://www.tradingview.com/script/xzIoaIJC-SSL-channel/
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Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L1025
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Usage:
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dataframe['sslDown'], dataframe['sslUp'] = SSLChannels(dataframe, 10)
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"""
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if mode not in ('sma'):
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raise ValueError(f"Mode {mode} not supported yet")
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df = dataframe.copy()
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if mode == 'sma':
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df['smaHigh'] = df['high'].rolling(length).mean()
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df['smaLow'] = df['low'].rolling(length).mean()
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df['hlv'] = np.where(df['close'] > df['smaHigh'], 1,
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np.where(df['close'] < df['smaLow'], -1, np.NAN))
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df['hlv'] = df['hlv'].ffill()
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df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
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df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
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return df['sslDown'], df['sslUp']
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def SSLChannels_ATR(dataframe, length=7):
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"""
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SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/
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Credit to @JimmyNixx for python
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"""
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df = dataframe.copy()
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df['ATR'] = ta.ATR(df, timeperiod=14)
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df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
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df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
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df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
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df['hlv'] = df['hlv'].ffill()
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df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
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df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
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return df['sslDown'], df['sslUp']
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def WaveTrend(dataframe, chlen=10, avg=21, smalen=4):
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"""
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WaveTrend Ocillator by LazyBear
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https://www.tradingview.com/script/2KE8wTuF-Indicator-WaveTrend-Oscillator-WT/
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"""
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df = dataframe.copy()
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df['hlc3'] = (df['high'] + df['low'] + df['close']) / 3
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df['esa'] = ta.EMA(df['hlc3'], timeperiod=chlen)
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df['d'] = ta.EMA((df['hlc3'] - df['esa']).abs(), timeperiod=chlen)
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df['ci'] = (df['hlc3'] - df['esa']) / (0.015 * df['d'])
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df['tci'] = ta.EMA(df['ci'], timeperiod=avg)
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df['wt1'] = df['tci']
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df['wt2'] = ta.SMA(df['wt1'], timeperiod=smalen)
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df['wt1-wt2'] = df['wt1'] - df['wt2']
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return df['wt1'], df['wt2']
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def T3(dataframe, length=5):
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"""
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T3 Average by HPotter on Tradingview
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https://www.tradingview.com/script/qzoC9H1I-T3-Average/
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"""
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df = dataframe.copy()
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df['xe1'] = ta.EMA(df['close'], timeperiod=length)
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df['xe2'] = ta.EMA(df['xe1'], timeperiod=length)
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df['xe3'] = ta.EMA(df['xe2'], timeperiod=length)
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df['xe4'] = ta.EMA(df['xe3'], timeperiod=length)
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df['xe5'] = ta.EMA(df['xe4'], timeperiod=length)
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df['xe6'] = ta.EMA(df['xe5'], timeperiod=length)
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b = 0.7
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c1 = -b * b * b
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c2 = 3 * b * b + 3 * b * b * b
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c3 = -6 * b * b - 3 * b - 3 * b * b * b
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c4 = 1 + 3 * b + b * b * b + 3 * b * b
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df['T3Average'] = c1 * df['xe6'] + c2 * df['xe5'] + c3 * df['xe4'] + c4 * df['xe3']
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return df['T3Average']
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def SROC(dataframe, roclen=21, emalen=13, smooth=21):
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df = dataframe.copy()
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roc = ta.ROC(df, timeperiod=roclen)
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ema = ta.EMA(df, timeperiod=emalen)
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sroc = ta.ROC(ema, timeperiod=smooth)
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return sroc
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