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Revise plot_profit to use pandas functions where possible
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Matthias
parent
8aa327cb8a
commit
c3db4ebbc3
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@ -4,64 +4,28 @@ Script to display profits
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Use `python plot_profit.py --help` to display the command line arguments
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"""
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import json
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import logging
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import sys
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from argparse import Namespace
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from pathlib import Path
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from typing import List, Optional
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from typing import List
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import numpy as np
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import pandas as pd
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import plotly.graph_objs as go
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from plotly import tools
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from plotly.offline import plot
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from freqtrade.arguments import Arguments, ARGS_PLOT_PROFIT
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from freqtrade.arguments import ARGS_PLOT_PROFIT, Arguments
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from freqtrade.configuration import Configuration
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from freqtrade.data import history
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from freqtrade.exchange import timeframe_to_seconds
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from freqtrade.misc import common_datearray
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from freqtrade.data.btanalysis import create_cum_profit, load_trades
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from freqtrade.plot.plotting import generate_plot_file
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from freqtrade.resolvers import StrategyResolver
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from freqtrade.state import RunMode
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logger = logging.getLogger(__name__)
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# data:: [ pair, profit-%, enter, exit, time, duration]
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# data:: ["ETH/BTC", 0.0023975, "1515598200", "1515602100", "2018-01-10 07:30:00+00:00", 65]
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def make_profit_array(data: List, px: int, min_date: int,
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interval: str,
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filter_pairs: Optional[List] = None) -> np.ndarray:
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pg = np.zeros(px)
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filter_pairs = filter_pairs or []
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# Go through the trades
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# and make an total profit
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# array
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for trade in data:
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pair = trade[0]
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if filter_pairs and pair not in filter_pairs:
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continue
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profit = trade[1]
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trade_sell_time = int(trade[3])
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ix = define_index(min_date, trade_sell_time, interval)
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if ix < px:
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logger.debug('[%s]: Add profit %s on %s', pair, profit, trade[4])
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pg[ix] += profit
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# rewrite the pg array to go from
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# total profits at each timeframe
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# to accumulated profits
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pa = 0
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for x in range(0, len(pg)):
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p = pg[x] # Get current total percent
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pa += p # Add to the accumulated percent
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pg[x] = pa # write back to save memory
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return pg
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def plot_profit(args: Namespace) -> None:
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"""
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Plots the total profit for all pairs.
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@ -70,34 +34,15 @@ def plot_profit(args: Namespace) -> None:
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in helping out to find a good algorithm.
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"""
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# We need to use the same pairs, same ticker_interval
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# and same timeperiod as used in backtesting
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# to match the tickerdata against the profits-results
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# We need to use the same pairs and the same ticker_interval
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# as used in backtesting / trading
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# to match the tickerdata against the results
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timerange = Arguments.parse_timerange(args.timerange)
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config = Configuration(args, RunMode.OTHER).get_config()
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# Init strategy
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try:
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strategy = StrategyResolver({'strategy': config.get('strategy')}).strategy
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except AttributeError:
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logger.critical(
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'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
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config.get('strategy')
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)
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exit(1)
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# Load the profits results
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try:
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filename = args.exportfilename
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with open(filename) as file:
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data = json.load(file)
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except FileNotFoundError:
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logger.critical(
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'File "backtest-result.json" not found. This script require backtesting '
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'results to run.\nPlease run a backtesting with the parameter --export.')
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exit(1)
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strategy = StrategyResolver(config).strategy
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# Take pairs from the cli otherwise switch to the pair in the config file
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if args.pairs:
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@ -106,6 +51,11 @@ def plot_profit(args: Namespace) -> None:
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else:
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filter_pairs = config['exchange']['pair_whitelist']
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# Load the profits results
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trades = load_trades(config)
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trades = trades[trades['pair'].isin(filter_pairs)]
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ticker_interval = strategy.ticker_interval
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pairs = config['exchange']['pair_whitelist']
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@ -120,49 +70,28 @@ def plot_profit(args: Namespace) -> None:
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refresh_pairs=False,
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timerange=timerange
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)
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dataframes = strategy.tickerdata_to_dataframe(tickers)
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# NOTE: the dataframes are of unequal length,
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# 'dates' is an merged date array of them all.
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dates = common_datearray(dataframes)
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min_date = int(min(dates).timestamp())
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max_date = int(max(dates).timestamp())
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num_iterations = define_index(min_date, max_date, ticker_interval) + 1
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# Make an average close price of all the pairs that was involved.
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# Create an average close price of all the pairs that were involved.
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# this could be useful to gauge the overall market trend
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# We are essentially saying:
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# array <- sum dataframes[*]['close'] / num_items dataframes
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# FIX: there should be some onliner numpy/panda for this
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avgclose = np.zeros(num_iterations)
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num = 0
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for pair, pair_data in dataframes.items():
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close = pair_data['close']
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maxprice = max(close) # Normalize price to [0,1]
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logger.info('Pair %s has length %s' % (pair, len(close)))
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for x in range(0, len(close)):
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avgclose[x] += close[x] / maxprice
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# avgclose += close
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num += 1
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avgclose /= num
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# make an profits-growth array
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pg = make_profit_array(data, num_iterations, min_date, ticker_interval, filter_pairs)
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# Combine close-values for all pairs, rename columns to "pair"
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df_comb = pd.concat([tickers[pair].set_index('date').rename(
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{'close': pair}, axis=1)[pair] for pair in tickers], axis=1)
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df_comb['mean'] = df_comb.mean(axis=1)
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# Add combined cumulative profit
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df_comb = create_cum_profit(df_comb, trades, 'cum_profit')
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#
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# Plot the pairs average close prices, and total profit growth
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#
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avgclose = go.Scattergl(
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x=dates,
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y=avgclose,
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x=df_comb.index,
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y=df_comb['mean'],
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name='Avg close price',
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)
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profit = go.Scattergl(
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x=dates,
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y=pg,
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x=df_comb.index,
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y=df_comb['cum_profit'],
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name='Profit',
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)
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@ -172,23 +101,19 @@ def plot_profit(args: Namespace) -> None:
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fig.append_trace(profit, 2, 1)
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for pair in pairs:
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pg = make_profit_array(data, num_iterations, min_date, ticker_interval, [pair])
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profit_col = f'cum_profit_{pair}'
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df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col)
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pair_profit = go.Scattergl(
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x=dates,
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y=pg,
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name=pair,
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x=df_comb.index,
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y=df_comb[profit_col],
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name=f"Profit {pair}",
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)
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fig.append_trace(pair_profit, 3, 1)
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plot(fig, filename=str(Path('user_data').joinpath('freqtrade-profit-plot.html')))
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def define_index(min_date: int, max_date: int, ticker_interval: str) -> int:
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"""
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Return the index of a specific date
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"""
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interval_seconds = timeframe_to_seconds(ticker_interval)
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return int((max_date - min_date) / interval_seconds)
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generate_plot_file(fig,
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filename='freqtrade-profit-plot.html',
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auto_open=True)
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def plot_parse_args(args: List[str]) -> Namespace:
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