stable/scripts/plot_profit.py

159 lines
4.5 KiB
Python
Executable File

#!/usr/bin/env python3
import sys
import json
import numpy as np
import plotly
from plotly import tools
from plotly.offline import plot
import plotly.graph_objs as go
import freqtrade.optimize as optimize
import freqtrade.misc as misc
import freqtrade.exchange as exchange
from freqtrade.strategy.strategy import Strategy
def plot_parse_args(args):
parser = misc.common_args_parser('Graph profits')
# FIX: perhaps delete those backtesting options that are not feasible (shows up in -h)
misc.backtesting_options(parser)
misc.scripts_options(parser)
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]
ix = tim + dur - 1
if ix < px:
pg[ix] += 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)
config.update({'strategy': args.strategy})
# Init strategy
strategy = Strategy()
strategy.init(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)
timerange = misc.parse_timerange(args.timerange)
tickers = optimize.load_data(args.datadir, pairs=pairs,
ticker_interval=strategy.ticker_interval,
refresh_pairs=False,
timerange=timerange)
dataframes = optimize.preprocess(tickers)
# NOTE: the dataframes are of unequal length,
# 'dates' is an merged date array of them all.
dates = misc.common_datearray(dataframes)
max_x = dates.size
# Make an average close price of all the pairs that was involved.
# this could be useful to gauge the overall market trend
# 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
#
avgclose = go.Scattergl(
x=dates,
y=avgclose,
name='Avg close price',
)
profit = go.Scattergl(
x=dates,
y=pg,
name='Profit',
)
fig = tools.make_subplots(rows=3, cols=1, shared_xaxes=True, row_width=[1, 1, 1])
fig.append_trace(avgclose, 1, 1)
fig.append_trace(profit, 2, 1)
for pair in pairs:
pg = make_profit_array(data, max_x, pair)
pair_profit = go.Scattergl(
x=dates,
y=pg,
name=pair,
)
fig.append_trace(pair_profit, 3, 1)
plot(fig, filename='freqtrade-profit-plot.html')
if __name__ == '__main__':
args = plot_parse_args(sys.argv[1:])
plot_profit(args)