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small tweaks to buy strategy and it's visualization

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This commit is contained in:
Janne Sinivirta 2017-10-15 16:54:26 +03:00
parent 58f34d4f4b
commit bf6f563df2
1 changed files with 14 additions and 12 deletions
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26
freqtrade/analyze.py
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@ -31,15 +31,13 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
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['sma'] = ta.SMA(dataframe, timeperiod=30)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['mfi'] = ta.MFI(dataframe)
return dataframe
@ -50,14 +48,12 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(dataframe['close'] < dataframe['sma']) &
(dataframe['cci'] < -100) &
(dataframe['tema'] <= dataframe['blower']) &
(dataframe['mfi'] < 30) &
(dataframe['fastd'] < 20) &
(dataframe['adx'] > 20),
(dataframe['mfi'] < 25) &
(dataframe['fastd'] < 25) &
(dataframe['adx'] > 30),
'buy'] = 1
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
@ -120,20 +116,26 @@ def plot_dataframe(dataframe: DataFrame, pair: str) -> None:
import matplotlib.pyplot as plt
# Two subplots sharing x axis
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.suptitle(pair, fontsize=14, fontweight='bold')
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['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
ax1.plot(dataframe.index.values, dataframe['tema'], ':', label='TEMA')
ax1.plot(dataframe.index.values, dataframe['blower'], '-.', label='BB low')
ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
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, dataframe['mfi'], label='MFI')
# ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
ax2.legend()
ax3.plot(dataframe.index.values, dataframe['fastk'], label='k')
ax3.plot(dataframe.index.values, dataframe['fastd'], label='d')
ax3.plot(dataframe.index.values, [20] * len(dataframe.index.values))
ax3.legend()
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
fig.subplots_adjust(hspace=0)