156 lines
5.0 KiB
Python
Executable File
156 lines
5.0 KiB
Python
Executable File
#!/usr/bin/env python3
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import sys
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import argparse
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import json
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import matplotlib.pyplot as plt
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import numpy as np
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import freqtrade.optimize as optimize
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import freqtrade.misc as misc
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import freqtrade.exchange as exchange
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import freqtrade.analyze as analyze
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def plot_parse_args(args ):
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parser = misc.common_args_parser('Graph utility')
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# FIX: perhaps delete those backtesting options that are not feasible (shows up in -h)
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misc.backtesting_options(parser)
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parser.add_argument(
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'-p', '--pair',
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help = 'Show profits for only this pairs. Pairs are comma-separated.',
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dest = 'pair',
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default = None
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)
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return parser.parse_args(args)
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# data:: [ pair, profit-%, enter, exit, time, duration]
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# data:: ['BTC_XMR', 0.00537847, '1511176800', '1511178000', 5057, 1]
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# FIX: make use of the enter/exit dates to insert the
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# profit more precisely into the pg array
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def make_profit_array(data, px, filter_pairs=[]):
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pg = np.zeros(px)
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# Go through the trades
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# and make an total profit
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# array
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for trade in data:
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pair = trade[0]
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if filter_pairs and pair not in filter_pairs:
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continue
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profit = trade[1]
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tim = trade[4]
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dur = trade[5]
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pg[tim+dur-1] += profit
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# rewrite the pg array to go from
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# total profits at each timeframe
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# to accumulated profits
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pa = 0
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for x in range(0,len(pg)):
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p = pg[x] # Get current total percent
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pa += p # Add to the accumulated percent
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pg[x] = pa # write back to save memory
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return pg
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def plot_profit(args) -> None:
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"""
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Plots the total profit for all pairs.
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Note, the profit calculation isn't realistic.
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But should be somewhat proportional, and therefor useful
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in helping out to find a good algorithm.
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"""
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# We need to use the same pairs, same tick_interval
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# and same timeperiod as used in backtesting
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# to match the tickerdata against the profits-results
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filter_pairs = args.pair
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config = misc.load_config(args.config)
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pairs = config['exchange']['pair_whitelist']
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if filter_pairs:
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filter_pairs = filter_pairs.split(',')
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pairs = list(set(pairs) & set(filter_pairs))
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print('Filter, keep pairs %s' % pairs)
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tickers = optimize.load_data(args.datadir, pairs=pairs,
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ticker_interval=args.ticker_interval,
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refresh_pairs=False)
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dataframes = optimize.preprocess(tickers)
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# Make an average close price of all the pairs that was involved.
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# this could be useful to gauge the overall market trend
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# FIX: since the dataframes are of unequal length,
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# andor has different dates, we need to merge them
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# But we dont have the date information in the
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# backtesting results, this is needed to match the dates
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# For now, assume the dataframes are aligned.
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max_x = 0
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for pair, pair_data in dataframes.items():
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n = len(pair_data['close'])
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max_x = max(max_x, n)
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# if max_x != n:
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# raise Exception('Please rerun script. Input data has different lengths %s'
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# %('Different pair length: %s <=> %s' %(max_x, n)))
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print('max_x: %s' %(max_x))
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# We are essentially saying:
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# array <- sum dataframes[*]['close'] / num_items dataframes
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# FIX: there should be some onliner numpy/panda for this
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avgclose = np.zeros(max_x)
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num = 0
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for pair, pair_data in dataframes.items():
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close = pair_data['close']
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maxprice = max(close) # Normalize price to [0,1]
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print('Pair %s has length %s' %(pair, len(close)))
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for x in range(0, len(close)):
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avgclose[x] += close[x] / maxprice
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# avgclose += close
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num += 1
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avgclose /= num
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# Load the profits results
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# And make an profits-growth array
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filename = 'backtest-result.json'
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with open(filename) as file:
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data = json.load(file)
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pg = make_profit_array(data, max_x, filter_pairs)
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#
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# Plot the pairs average close prices, and total profit growth
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#
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fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
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fig.suptitle('total profit')
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ax1.plot(avgclose, label='avgclose')
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ax2.plot(pg, label='profit')
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ax1.legend(loc='upper left')
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ax2.legend(loc='upper left')
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# FIX if we have one line pair in paris
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# then skip the plotting of the third graph,
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# or change what we plot
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# In third graph, we plot each profit separately
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for pair in pairs:
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pg = make_profit_array(data, max_x, pair)
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ax3.plot(pg, label=pair)
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ax3.legend(loc='upper left')
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# black background to easier see multiple colors
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ax3.set_facecolor('black')
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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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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_profit(args)
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