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improved buy signal strategy

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This commit is contained in:
Janne Sinivirta 2017-09-29 09:37:45 +03:00
parent 272abed807
commit 44cdf3e0c2
1 changed files with 19 additions and 25 deletions
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44
analyze.py
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@ -43,15 +43,20 @@ def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame
.sort_values('date') .sort_values('date')
return df[df['date'].map(arrow.get) > minimum_date] return df[df['date'].map(arrow.get) > minimum_date]
def populate_indicators(dataframe: DataFrame) -> DataFrame: def populate_indicators(dataframe: DataFrame) -> DataFrame:
""" """
Adds several different TA indicators to the given DataFrame Adds several different TA indicators to the given DataFrame
""" """
dataframe['ema'] = ta.EMA(dataframe, timeperiod=33)
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22) dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
dataframe['adx'] = ta.ADX(dataframe) dataframe['adx'] = ta.ADX(dataframe)
stoch = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
dataframe['cci'] = ta.CCI(dataframe, timeperiod=5)
dataframe['sma'] = ta.SMA(dataframe, timeperiod=100)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=4)
dataframe['mfi'] = ta.MFI(dataframe)
return dataframe return dataframe
@ -61,26 +66,14 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
:param dataframe: DataFrame :param dataframe: DataFrame
:return: DataFrame with buy column :return: DataFrame with buy column
""" """
prev_sar = dataframe['sar'].shift(1)
prev_close = dataframe['close'].shift(1)
prev_sar2 = dataframe['sar'].shift(2)
prev_close2 = dataframe['close'].shift(2)
# wait for stable turn from bearish to bullish market
dataframe.loc[
(dataframe['close'] > dataframe['sar']) &
(prev_close > prev_sar) &
(prev_close2 < prev_sar2),
'swap'
] = 1
# consider prices above ema to be in upswing
dataframe.loc[dataframe['ema'] <= dataframe['close'], 'upswing'] = 1
dataframe.loc[ dataframe.loc[
(dataframe['upswing'] == 1) & (dataframe['close'] < dataframe['sma']) &
(dataframe['swap'] == 1) & (dataframe['cci'] < -100) &
(dataframe['adx'] > 25), # adx over 25 tells there's enough momentum (dataframe['tema'] <= dataframe['blower']) &
(dataframe['mfi'] < 30) &
(dataframe['fastd'] < 20) &
(dataframe['adx'] > 20),
'buy'] = 1 'buy'] = 1
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close'] dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
@ -147,12 +140,13 @@ def plot_dataframe(dataframe: DataFrame, pair: str) -> None:
ax1.plot(dataframe.index.values, dataframe['sar'], 'g_', label='pSAR') ax1.plot(dataframe.index.values, dataframe['sar'], 'g_', label='pSAR')
ax1.plot(dataframe.index.values, dataframe['close'], label='close') ax1.plot(dataframe.index.values, dataframe['close'], label='close')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell') # ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['ema'], '--', label='EMA(20)') ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
ax1.plot(dataframe.index.values, dataframe['buy'], 'bo', label='buy') ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
ax1.legend() ax1.legend()
ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX') # ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values)) ax2.plot(dataframe.index.values, dataframe['mfi'], label='MFI')
# ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
ax2.legend() ax2.legend()
# Fine-tune figure; make subplots close to each other and hide x ticks for # Fine-tune figure; make subplots close to each other and hide x ticks for