229 lines
6.7 KiB
Python
Executable File
229 lines
6.7 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
"""
|
|
Script to display profits
|
|
|
|
Mandatory Cli parameters:
|
|
-p / --pair: pair to examine
|
|
|
|
Optional Cli parameters
|
|
-c / --config: specify configuration file
|
|
-s / --strategy: strategy to use
|
|
-d / --datadir: path to pair backtest data
|
|
--timerange: specify what timerange of data to use
|
|
--export-filename: Specify where the backtest export is located.
|
|
"""
|
|
import logging
|
|
import os
|
|
import sys
|
|
import json
|
|
from argparse import Namespace
|
|
from typing import List, Optional
|
|
import numpy as np
|
|
|
|
from plotly import tools
|
|
from plotly.offline import plot
|
|
import plotly.graph_objs as go
|
|
|
|
from freqtrade.arguments import Arguments
|
|
from freqtrade.configuration import Configuration
|
|
from freqtrade import constants
|
|
from freqtrade.data as history
|
|
from freqtrade.resolvers import StrategyResolver
|
|
import freqtrade.misc as misc
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
# data:: [ pair, profit-%, enter, exit, time, duration]
|
|
# data:: ["ETH/BTC", 0.0023975, "1515598200", "1515602100", "2018-01-10 07:30:00+00:00", 65]
|
|
def make_profit_array(data: List, px: int, min_date: int,
|
|
interval: int,
|
|
filter_pairs: Optional[List] = None) -> np.ndarray:
|
|
pg = np.zeros(px)
|
|
filter_pairs = filter_pairs or []
|
|
# 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]
|
|
trade_sell_time = int(trade[3])
|
|
|
|
ix = define_index(min_date, trade_sell_time, interval)
|
|
if ix < px:
|
|
logger.debug('[%s]: Add profit %s on %s', pair, profit, trade[4])
|
|
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: Namespace) -> 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
|
|
timerange = Arguments.parse_timerange(args.timerange)
|
|
|
|
config = Configuration(args).get_config()
|
|
|
|
# Init strategy
|
|
try:
|
|
strategy = StrategyResolver({'strategy': config.get('strategy')}).strategy
|
|
|
|
except AttributeError:
|
|
logger.critical(
|
|
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
|
|
config.get('strategy')
|
|
)
|
|
exit(1)
|
|
|
|
# Load the profits results
|
|
try:
|
|
filename = args.exportfilename
|
|
with open(filename) as file:
|
|
data = json.load(file)
|
|
except FileNotFoundError:
|
|
logger.critical(
|
|
'File "backtest-result.json" not found. This script require backtesting '
|
|
'results to run.\nPlease run a backtesting with the parameter --export.')
|
|
exit(1)
|
|
|
|
# Take pairs from the cli otherwise switch to the pair in the config file
|
|
if args.pair:
|
|
filter_pairs = args.pair
|
|
filter_pairs = filter_pairs.split(',')
|
|
else:
|
|
filter_pairs = config['exchange']['pair_whitelist']
|
|
|
|
tick_interval = strategy.ticker_interval
|
|
pairs = config['exchange']['pair_whitelist']
|
|
|
|
if filter_pairs:
|
|
pairs = list(set(pairs) & set(filter_pairs))
|
|
logger.info('Filter, keep pairs %s' % pairs)
|
|
|
|
tickers = history.load_data(
|
|
datadir=config.get('datadir'),
|
|
pairs=pairs,
|
|
ticker_interval=tick_interval,
|
|
refresh_pairs=False,
|
|
timerange=timerange
|
|
)
|
|
dataframes = strategy.tickerdata_to_dataframe(tickers)
|
|
|
|
# NOTE: the dataframes are of unequal length,
|
|
# 'dates' is an merged date array of them all.
|
|
|
|
dates = misc.common_datearray(dataframes)
|
|
min_date = int(min(dates).timestamp())
|
|
max_date = int(max(dates).timestamp())
|
|
num_iterations = define_index(min_date, max_date, tick_interval) + 1
|
|
|
|
# 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(num_iterations)
|
|
num = 0
|
|
for pair, pair_data in dataframes.items():
|
|
close = pair_data['close']
|
|
maxprice = max(close) # Normalize price to [0,1]
|
|
logger.info('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
|
|
|
|
# make an profits-growth array
|
|
pg = make_profit_array(data, num_iterations, min_date, tick_interval, 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, num_iterations, min_date, tick_interval, pair)
|
|
pair_profit = go.Scattergl(
|
|
x=dates,
|
|
y=pg,
|
|
name=pair,
|
|
)
|
|
fig.append_trace(pair_profit, 3, 1)
|
|
|
|
plot(fig, filename=os.path.join('user_data', 'freqtrade-profit-plot.html'))
|
|
|
|
|
|
def define_index(min_date: int, max_date: int, interval: str) -> int:
|
|
"""
|
|
Return the index of a specific date
|
|
"""
|
|
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
|
|
return int((max_date - min_date) / (interval_minutes * 60))
|
|
|
|
|
|
def plot_parse_args(args: List[str]) -> Namespace:
|
|
"""
|
|
Parse args passed to the script
|
|
:param args: Cli arguments
|
|
:return: args: Array with all arguments
|
|
"""
|
|
arguments = Arguments(args, 'Graph profits')
|
|
arguments.scripts_options()
|
|
arguments.common_args_parser()
|
|
arguments.optimizer_shared_options(arguments.parser)
|
|
arguments.backtesting_options(arguments.parser)
|
|
|
|
return arguments.parse_args()
|
|
|
|
|
|
def main(sysargv: List[str]) -> None:
|
|
"""
|
|
This function will initiate the bot and start the trading loop.
|
|
:return: None
|
|
"""
|
|
logger.info('Starting Plot Dataframe')
|
|
plot_profit(
|
|
plot_parse_args(sysargv)
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main(sys.argv[1:])
|