101 lines
3.4 KiB
Python
Executable File
101 lines
3.4 KiB
Python
Executable File
#!/usr/bin/env python3
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import sys
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import logging
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import argparse
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import matplotlib
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# matplotlib.use("Qt5Agg")
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import matplotlib.dates as mdates
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import matplotlib.pyplot as plt
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from pandas import DataFrame
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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 freqtrade import exchange, analyze
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from freqtrade.misc import common_args_parser
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from freqtrade.strategy.strategy import Strategy
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import freqtrade.misc as misc
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import freqtrade.optimize as optimize
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import freqtrade.analyze as analyze
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logger = logging.getLogger(__name__)
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def plot_parse_args(args):
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parser = misc.common_args_parser('Graph dataframe')
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misc.backtesting_options(parser)
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misc.scripts_options(parser)
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return parser.parse_args(args)
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def plot_analyzed_dataframe(args) -> None:
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"""
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Calls analyze() and plots the returned dataframe
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:param pair: pair as str
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:return: None
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"""
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pair = args.pair
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pairs = [pair]
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timerange = misc.parse_timerange(args.timerange)
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# Init strategy
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strategy = Strategy()
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strategy.init({'strategy': args.strategy})
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tick_interval = strategy.ticker_interval
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tickers = {}
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if args.live:
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logger.info('Downloading pair.')
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# Init Bittrex to use public API
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exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
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tickers[pair] = exchange.get_ticker_history(pair, tick_interval)
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else:
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tickers = optimize.load_data(args.datadir, pairs=pairs,
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ticker_interval=tick_interval,
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refresh_pairs=False,
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timerange=timerange)
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dataframes = optimize.tickerdata_to_dataframe(tickers)
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dataframe = dataframes[pair]
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dataframe = analyze.populate_buy_trend(dataframe)
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dataframe = analyze.populate_sell_trend(dataframe)
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dates = misc.datesarray_to_datetimearray(dataframe['date'])
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# Two subplots sharing x axis
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fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
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fig.suptitle(pair + " " + str(tick_interval), fontsize=14, fontweight='bold')
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ax1.plot(dates, dataframe['close'], label='close')
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# ax1.plot(dates, dataframe['sell'], 'ro', label='sell')
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ax1.plot(dates, dataframe['sma'], '--', label='SMA')
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ax1.plot(dates, dataframe['tema'], ':', label='TEMA')
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ax1.plot(dates, dataframe['blower'], '-.', label='BB low')
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ax1.plot(dates, dataframe['close'] * dataframe['buy'], 'bo', label='buy')
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ax1.plot(dates, dataframe['close'] * dataframe['sell'], 'ro', label='sell')
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ax1.legend()
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ax2.plot(dates, dataframe['adx'], label='ADX')
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ax2.plot(dates, dataframe['mfi'], label='MFI')
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# ax2.plot(dates, [25] * len(dataframe.index.values))
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ax2.legend()
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ax3.plot(dates, dataframe['fastk'], label='k')
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ax3.plot(dates, dataframe['fastd'], label='d')
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ax3.plot(dates, [20] * len(dataframe.index.values))
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ax3.legend()
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xfmt = mdates.DateFormatter('%d-%m-%y %H:%M') # Dont let matplotlib autoformat date
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ax3.xaxis.set_major_formatter(xfmt)
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# Fine-tune figure; make subplots close to each other and hide x ticks for
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# all but bottom plot.
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fig.subplots_adjust(hspace=0)
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fig.autofmt_xdate() # Rotate the dates
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plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
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plt.show()
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if __name__ == '__main__':
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args = plot_parse_args(sys.argv[1:])
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plot_analyzed_dataframe(args)
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