Merge branch 'develop' into support_multiple_ticker

scripts/plot_dataframe.py

@@ -28,7 +28,7 @@ def plot_parse_args(args ):
     return parser.parse_args(args)
 
 
-def plot_analyzed_dataframe(args) -> None:
+def plot_analyzed_dataframe(args):
     """
     Calls analyze() and plots the returned dataframe
     :param pair: pair as str

scripts/plot_profit.py

@@ -0,0 +1,155 @@
+#!/usr/bin/env python3
+
+import sys
+import argparse
+import json
+import matplotlib.pyplot as plt
+import numpy as np
+
+import freqtrade.optimize as optimize
+import freqtrade.misc as misc
+import freqtrade.exchange as exchange
+import freqtrade.analyze  as analyze
+
+
+def plot_parse_args(args ):
+    parser = misc.common_args_parser('Graph utility')
+    # FIX: perhaps delete those backtesting options that are not feasible (shows up in -h)
+    misc.backtesting_options(parser)
+    parser.add_argument(
+        '-p', '--pair',
+        help = 'Show profits for only this pairs. Pairs are comma-separated.',
+        dest = 'pair',
+        default = None
+    )
+    return parser.parse_args(args)
+
+
+# data:: [ pair,      profit-%,  enter,         exit,        time, duration]
+# data:: ['BTC_XMR', 0.00537847, '1511176800', '1511178000', 5057, 1]
+# FIX: make use of the enter/exit dates to insert the
+# profit more precisely into the pg array
+def make_profit_array(data, px, filter_pairs=[]):
+    pg = np.zeros(px)
+    # Go through the trades
+    # and make an total profit
+    # array
+    for trade in data:
+        pair = trade[0]
+        if filter_pairs and pair not in filter_pairs:
+            continue
+        profit = trade[1]
+        tim = trade[4]
+        dur = trade[5]
+        pg[tim+dur-1] += profit
+
+    # rewrite the pg array to go from
+    # total profits at each timeframe
+    # to accumulated profits
+    pa = 0
+    for x in range(0,len(pg)):
+        p = pg[x]  # Get current total percent
+        pa += p  # Add to the accumulated percent
+        pg[x] = pa  # write back to save memory
+
+    return pg
+
+
+def plot_profit(args) -> None:
+    """
+    Plots the total profit for all pairs.
+    Note, the profit calculation isn't realistic.
+    But should be somewhat proportional, and therefor useful
+    in helping out to find a good algorithm.
+    """
+
+    # We need to use the same pairs, same tick_interval
+    # and same timeperiod as used in backtesting
+    # to match the tickerdata against the profits-results
+
+    filter_pairs = args.pair
+
+    config = misc.load_config(args.config)
+    pairs = config['exchange']['pair_whitelist']
+    if filter_pairs:
+        filter_pairs = filter_pairs.split(',')
+        pairs = list(set(pairs) & set(filter_pairs))
+        print('Filter, keep pairs %s' % pairs)
+
+    tickers = optimize.load_data(args.datadir, pairs=pairs,
+                                 ticker_interval=args.ticker_interval,
+                                 refresh_pairs=False)
+    dataframes = optimize.preprocess(tickers)
+
+    # Make an average close price of all the pairs that was involved.
+    # this could be useful to gauge the overall market trend
+
+    # FIX: since the dataframes are of unequal length,
+    # andor has different dates, we need to merge them
+    # But we dont have the date information in the
+    # backtesting results, this is needed to match the dates
+    # For now, assume the dataframes are aligned.
+    max_x = 0
+    for pair, pair_data in dataframes.items():
+        n = len(pair_data['close'])
+        max_x = max(max_x, n)
+    #    if max_x != n:
+    #        raise Exception('Please rerun script. Input data has different lengths %s'
+    #                         %('Different pair length: %s <=> %s' %(max_x, n)))
+    print('max_x: %s' %(max_x))
+
+    # We are essentially saying:
+    #  array <- sum dataframes[*]['close'] / num_items dataframes
+    #  FIX: there should be some onliner numpy/panda for this
+    avgclose = np.zeros(max_x)
+    num = 0
+    for pair, pair_data in dataframes.items():
+        close = pair_data['close']
+        maxprice = max(close)  # Normalize price to [0,1]
+        print('Pair %s has length %s' %(pair, len(close)))
+        for x in range(0, len(close)):
+            avgclose[x] += close[x] / maxprice
+        # avgclose += close
+        num += 1
+    avgclose /= num
+
+    # Load the profits results
+    # And make an profits-growth array
+
+    filename = 'backtest-result.json'
+    with open(filename) as file:
+      data = json.load(file)
+    pg = make_profit_array(data, max_x, filter_pairs)
+
+    #
+    # Plot the pairs average close prices, and total profit growth
+    #
+
+    fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
+    fig.suptitle('total profit')
+    ax1.plot(avgclose, label='avgclose')
+    ax2.plot(pg, label='profit')
+    ax1.legend(loc='upper left')
+    ax2.legend(loc='upper left')
+
+    # FIX if we have one line pair in paris
+    #     then skip the plotting of the third graph,
+    #     or change what we plot
+    # In third graph, we plot each profit separately
+    for pair in pairs:
+        pg = make_profit_array(data, max_x, pair)
+        ax3.plot(pg, label=pair)
+    ax3.legend(loc='upper left')
+    # black background to easier see multiple colors
+    ax3.set_facecolor('black')
+
+    # Fine-tune figure; make subplots close to each other and hide x ticks for
+    # all but bottom plot.
+    fig.subplots_adjust(hspace=0)
+    plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
+    plt.show()
+
+
+if __name__ == '__main__':
+    args = plot_parse_args(sys.argv[1:])
+    plot_profit(args)