script: plot profit

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kryofly 2018-01-12 10:55:49 +01:00
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@ -3,6 +3,7 @@ This page explains how to plot prices, indicator, profits.
## Table of Contents ## Table of Contents
- [Plot price and indicators](#plot-price-and-indicators) - [Plot price and indicators](#plot-price-and-indicators)
- [Plot profit](#plot-profit)
## Plot price and indicators ## Plot price and indicators
Usage for the price plotter: Usage for the price plotter:
@ -16,3 +17,32 @@ python script/plot_dataframe.py -p BTC_ETH
The -p pair argument, can be used to specify what The -p pair argument, can be used to specify what
pair you would like to plot. 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
```

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scripts/plot_profit.py Executable file
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#!/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
misc.backtesting_options(parser)
# TODO: Make the pair argument take a comma separated list
parser.add_argument(
'-p', '--pair',
help = 'Show profits for only this pair',
dest = 'pair',
default = None
)
return parser.parse_args(args)
def make_profit_array(data, filter_pair):
xmin = 0
xmax = 0
# pair profit-% time duration
# ['BTC_XMR', 0.00537847, 5057, 1]
for trade in data:
pair = trade[0]
profit = trade[1]
x = trade[2]
dur = trade[3]
xmax = max(xmax, x + dur)
pg = np.zeros(xmax)
# Go through the trades
# and make an total profit
# array
for trade in data:
pair = trade[0]
if filter_pair and pair != filter_pair:
continue
profit = trade[1]
tim = trade[2]
dur = trade[3]
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_pair = args.pair
config = misc.load_config(args.config)
pairs = config['exchange']['pair_whitelist']
if filter_pair:
print('Filtering out pair %s' % filter_pair)
pairs = list(filter(lambda pair: pair == filter_pair, 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.
# We are essentially saying:
# array <- sum dataframes[*]['close'] / num_items dataframes
# FIX: there should be some onliner numpy/panda for this
first = True
avgclose = None
num = 0
for pair, pair_data in dataframes.items():
close = pair_data['close']
print('Pair %s has length %s' %(pair, len(close)))
num += 1
if first:
first = False
avgclose = np.copy(close)
else:
avgclose += close
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, filter_pair)
#
# 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()
ax2.legend()
# 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, pair)
ax3.plot(pg, label=pair)
ax3.legend()
# 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)