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separate calculating indicators from parsing the data

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Janne Sinivirta 2017-09-09 13:02:47 +03:00
parent 00400d906d
commit 8a736ba38d
1 changed files with 10 additions and 3 deletions
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13
analyze.py
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@ -38,7 +38,6 @@ def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame
:param pair: pair as str in format BTC_ETH or BTC-ETH :param pair: pair as str in format BTC_ETH or BTC-ETH
:return: DataFrame :return: DataFrame
""" """
data = [{ data = [{
'close': t['C'], 'close': t['C'],
'volume': t['V'], 'volume': t['V'],
@ -47,8 +46,14 @@ def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame
'low': t['L'], 'low': t['L'],
'date': t['T'], 'date': t['T'],
} for t in sorted(ticker, key=lambda k: k['T']) if arrow.get(t['T']) > minimum_date] } for t in sorted(ticker, key=lambda k: k['T']) if arrow.get(t['T']) > minimum_date]
dataframe = DataFrame(json_normalize(data))
return DataFrame(json_normalize(data))
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30) dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30)
dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90) dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90)
@ -81,7 +86,7 @@ def populate_trends(dataframe: DataFrame) -> DataFrame:
""" """
dataframe.loc[ dataframe.loc[
(dataframe['stochrsi'] < 20) (dataframe['stochrsi'] < 20)
& (dataframe['macd'] > dataframe['macds']) & (dataframe['macd'] > dataframe['macds'])
& (dataframe['close'] > dataframe['sar']), & (dataframe['close'] > dataframe['sar']),
'underpriced' 'underpriced'
] = 1 ] = 1
@ -98,6 +103,7 @@ def get_buy_signal(pair: str) -> bool:
minimum_date = arrow.now() - timedelta(hours=6) minimum_date = arrow.now() - timedelta(hours=6)
data = get_ticker(pair, minimum_date) data = get_ticker(pair, minimum_date)
dataframe = parse_ticker_dataframe(data['result'], minimum_date) dataframe = parse_ticker_dataframe(data['result'], minimum_date)
dataframe = populate_indicators(dataframe)
dataframe = populate_trends(dataframe) dataframe = populate_trends(dataframe)
latest = dataframe.iloc[-1] latest = dataframe.iloc[-1]
@ -161,6 +167,7 @@ if __name__ == '__main__':
minimum_date = arrow.now() - timedelta(hours=6) minimum_date = arrow.now() - timedelta(hours=6)
data = get_ticker(pair, minimum_date) data = get_ticker(pair, minimum_date)
dataframe = parse_ticker_dataframe(data['result'], minimum_date) dataframe = parse_ticker_dataframe(data['result'], minimum_date)
dataframe = populate_indicators(dataframe)
dataframe = populate_trends(dataframe) dataframe = populate_trends(dataframe)
plot_dataframe(dataframe, pair) plot_dataframe(dataframe, pair)
time.sleep(60) time.sleep(60)