stable/scripts/plot_profit.py

#!/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
--timerange: specify what timerange of data to use.
"""

import sys
import json
from typing import Dict
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.analyze import Analyze
from freqtrade.logger import Logger

import freqtrade.optimize as optimize
import freqtrade.misc as misc


logger = Logger(name="Graph profits").get_logger()


# data:: [ pair,      profit-%,  enter,         exit,        time, duration]
# data:: ["BTC_ETH", 0.0023975, "1515598200", "1515602100", "2018-01-10 07:30:00+00:00", 65]
def make_profit_array(data, px, min_date, interval, 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]
        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) -> 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:
        analyze = Analyze({'strategy': config.get('strategy')})
    except AttributeError:
        logger.critical(
            'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
            config.get('strategy')
        )
        exit()

    # 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 = analyze.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 = optimize.load_data(
        datadir=args.datadir,
        pairs=pairs,
        ticker_interval=tick_interval,
        refresh_pairs=False,
        timerange=timerange
    )
    dataframes = analyze.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

    # Load the profits results
    # And make an profits-growth array

    try:
        filename = 'backtest-result.json'
        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(0)

    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='freqtrade-profit-plot.html')


def define_index(min_date, max_date, interval):
    """
    Return the index of a specific date
    """
    return int((max_date - min_date) / (interval * 60))


def plot_parse_args(args):
    """
    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: Dict) -> 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:])