Merge pull request #369 from kryofly/plot_profit

Plot profit from exported backtesting results

docs/plotting.md

@@ -0,0 +1,48 @@
+# Plotting
+This page explains how to plot prices, indicator, profits.
+
+## Table of Contents
+- [Plot price and indicators](#plot-price-and-indicators)
+- [Plot profit](#plot-profit)
+
+## Plot price and indicators
+Usage for the price plotter:
+script/plot_dataframe.py [-h] [-p pair]
+
+Example
+```
+python script/plot_dataframe.py -p BTC_ETH,BTC_LTC
+```
+
+The -p pair argument, can be used to specify what
+pair you would like to plot.
+
+
+## Plot profit
+
+The profit plotter show a picture with three plots:
+1) Average closing price for all pairs
+2) The summarized profit made by backtesting.
+   Note that this is not the real-world profit, but
+   more of an estimate.
+3) Each pair individually profit
+
+The first graph is good to get a grip of how the overall market
+progresses.
+
+The second graph will show how you algorithm works or doesnt.
+Perhaps you want an algorithm that steadily makes small profits,
+or one that acts less seldom, but makes big swings.
+
+The third graph can be useful to spot outliers, events in pairs
+that makes profit spikes.
+
+Usage for the profit plotter:
+script/plot_profit.py [-h] [-p pair] [--datadir directory] [--ticker_interval num]
+
+The -p pair argument, can be used to plot a single pair
+
+Example
+```
+python python scripts/plot_profit.py --datadir ../freqtrade/freqtrade/tests/testdata-20171221/ -p BTC_LTC
+```

freqtrade/misc.py

@@ -116,6 +116,14 @@ def common_args_parser(description: str):
         type=str,
         metavar='PATH',
     )
+    parser.add_argument(
+        '--datadir',
+        help='path to backtest data (default freqdata/tests/testdata)',
+        dest='datadir',
+        default=os.path.join('freqtrade', 'tests', 'testdata'),
+        type=str,
+        metavar='PATH',
+    )
     return parser
 
 
@@ -132,14 +140,6 @@ def parse_args(args: List[str], description: str):
         action='store_true',
         dest='dry_run_db',
     )
-    parser.add_argument(
-        '--datadir',
-        help='path to backtest data (default freqdata/tests/testdata',
-        dest='datadir',
-        default=os.path.join('freqtrade', 'tests', 'testdata'),
-        type=str,
-        metavar='PATH',
-    )
     parser.add_argument(
         '--dynamic-whitelist',
         help='dynamically generate and update whitelist \
@@ -155,22 +155,14 @@ def parse_args(args: List[str], description: str):
     return parser.parse_args(args)
 
 
-def build_subcommands(parser: argparse.ArgumentParser) -> None:
+def backtesting_options(parser: argparse.ArgumentParser) -> None:
-    """ Builds and attaches all subcommands """
+    parser.add_argument(
-    from freqtrade.optimize import backtesting, hyperopt
-
-    subparsers = parser.add_subparsers(dest='subparser')
-
-    # Add backtesting subcommand
-    backtesting_cmd = subparsers.add_parser('backtesting', help='backtesting module')
-    backtesting_cmd.set_defaults(func=backtesting.start)
-    backtesting_cmd.add_argument(
         '-l', '--live',
         action='store_true',
         dest='live',
         help='using live data',
     )
-    backtesting_cmd.add_argument(
+    parser.add_argument(
         '-i', '--ticker-interval',
         help='specify ticker interval in minutes (default: 5)',
         dest='ticker_interval',
@@ -178,28 +170,28 @@ def build_subcommands(parser: argparse.ArgumentParser) -> None:
         type=int,
         metavar='INT',
     )
-    backtesting_cmd.add_argument(
+    parser.add_argument(
         '--realistic-simulation',
         help='uses max_open_trades from config to simulate real world limitations',
         action='store_true',
         dest='realistic_simulation',
     )
-    backtesting_cmd.add_argument(
+    parser.add_argument(
         '-r', '--refresh-pairs-cached',
         help='refresh the pairs files in tests/testdata with the latest data from Bittrex. \
               Use it if you want to run your backtesting with up-to-date data.',
         action='store_true',
         dest='refresh_pairs',
     )
-    backtesting_cmd.add_argument(
+    parser.add_argument(
         '--export',
         help='Export backtest results, argument are: trades\
-              Example --export trades',
+              Example --export=trades',
         type=str,
         default=None,
         dest='export',
     )
-    backtesting_cmd.add_argument(
+    parser.add_argument(
         '--timerange',
         help='Specify what timerange of data to use.',
         default=None,
@@ -207,10 +199,9 @@ def build_subcommands(parser: argparse.ArgumentParser) -> None:
         dest='timerange',
     )
 
-    # Add hyperopt subcommand
+
-    hyperopt_cmd = subparsers.add_parser('hyperopt', help='hyperopt module')
+def hyperopt_options(parser: argparse.ArgumentParser) -> None:
-    hyperopt_cmd.set_defaults(func=hyperopt.start)
+    parser.add_argument(
-    hyperopt_cmd.add_argument(
         '-e', '--epochs',
         help='specify number of epochs (default: 100)',
         dest='epochs',
@@ -218,13 +209,13 @@ def build_subcommands(parser: argparse.ArgumentParser) -> None:
         type=int,
         metavar='INT',
     )
-    hyperopt_cmd.add_argument(
+    parser.add_argument(
         '--use-mongodb',
         help='parallelize evaluations with mongodb (requires mongod in PATH)',
         dest='mongodb',
         action='store_true',
     )
-    hyperopt_cmd.add_argument(
+    parser.add_argument(
         '-i', '--ticker-interval',
         help='specify ticker interval in minutes (default: 5)',
         dest='ticker_interval',
@@ -232,7 +223,7 @@ def build_subcommands(parser: argparse.ArgumentParser) -> None:
         type=int,
         metavar='INT',
     )
-    hyperopt_cmd.add_argument(
+    parser.add_argument(
         '--timerange',
         help='Specify what timerange of data to use.',
         default=None,
@@ -271,6 +262,23 @@ def parse_timerange(text):
     raise Exception('Incorrect syntax for timerange "%s"' % text)
 
 
+def build_subcommands(parser: argparse.ArgumentParser) -> None:
+    """ Builds and attaches all subcommands """
+    from freqtrade.optimize import backtesting, hyperopt
+
+    subparsers = parser.add_subparsers(dest='subparser')
+
+    # Add backtesting subcommand
+    backtesting_cmd = subparsers.add_parser('backtesting', help='backtesting module')
+    backtesting_cmd.set_defaults(func=backtesting.start)
+    backtesting_options(backtesting_cmd)
+
+    # Add hyperopt subcommand
+    hyperopt_cmd = subparsers.add_parser('hyperopt', help='hyperopt module')
+    hyperopt_cmd.set_defaults(func=hyperopt.start)
+    hyperopt_options(hyperopt_cmd)
+
+
 # Required json-schema for user specified config
 CONF_SCHEMA = {
     'type': 'object',

scripts/plot_dataframe.py

@@ -21,7 +21,7 @@ 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)