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Plot_profit.py: fix it and make it works with the new object model
This commit is contained in:
Gerald Lonlas
parent
9ae2491b1e
commit
45341bb246
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@ -1,44 +0,0 @@
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{
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"max_open_trades": 3,
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"stake_currency": "BTC",
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"stake_amount": 0.005,
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"fiat_display_currency": "USD",
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"dry_run": true,
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"unfilledtimeout": 600,
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"bid_strategy": {
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"ask_last_balance": 0.0
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},
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"exchange": {
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"name": "bittrex",
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"key": "",
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"secret": "",
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"pair_whitelist": [
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"BTC_ETH",
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"BTC_LTC",
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"BTC_ETC",
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"BTC_DASH",
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"BTC_ZEC",
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"BTC_XLM",
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"BTC_NXT",
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"BTC_POWR",
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"BTC_ADA",
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"BTC_XMR"
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],
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"pair_blacklist": [
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"BTC_DOGE"
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]
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},
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"experimental": {
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"use_sell_signal": false,
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"sell_profit_only": false
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},
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"telegram": {
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"enabled": true,
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"token": "387056091:AAEVz29u5KwphICqGB6c63RwZjqCd7Kh6T4",
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"chat_id": "391939601"
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},
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"initial_state": "running",
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"internals": {
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"process_throttle_secs": 5
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}
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}
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@ -1,31 +1,43 @@
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#!/usr/bin/env python3
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"""
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Script to display profits
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Mandatory Cli parameters:
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-p / --pair: pair to examine
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Optional Cli parameters
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-c / --config: specify configuration file
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-s / --strategy: strategy to use
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--timerange: specify what timerange of data to use.
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"""
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import sys
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import json
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from typing import Dict
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import numpy as np
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from datetime import datetime
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from plotly import tools
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from plotly.offline import plot
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import plotly.graph_objs as go
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from freqtrade.arguments import Arguments
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from freqtrade.configuration import Configuration
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from freqtrade.analyze import Analyze
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from freqtrade.logger import Logger
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import freqtrade.optimize as optimize
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import freqtrade.misc as misc
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from freqtrade.strategy.strategy import Strategy
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import pprint
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def plot_parse_args(args):
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parser = misc.common_args_parser('Graph profits')
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# FIX: perhaps delete those backtesting options that are not feasible (shows up in -h)
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misc.backtesting_options(parser)
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misc.scripts_options(parser)
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return parser.parse_args(args)
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logger = Logger(name="Graph profits").get_logger()
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# data:: [ pair, profit-%, enter, exit, time, duration]
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# data:: ['BTC_XMR', 0.00537847, '1511176800', '1511178000', 5057, 1]
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# FIX: make use of the enter/exit dates to insert the
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# profit more precisely into the pg array
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def make_profit_array(data, px, filter_pairs=[]):
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# data:: ["BTC_ETH", 0.0023975, "1515598200", "1515602100", "2018-01-10 07:30:00+00:00", 65]
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def make_profit_array(data, px, min_date, interval, filter_pairs=[]):
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pg = np.zeros(px)
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# Go through the trades
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# and make an total profit
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@ -35,10 +47,11 @@ def make_profit_array(data, px, filter_pairs=[]):
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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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tim = trade[4]
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dur = trade[5]
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ix = tim + dur - 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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@ -64,47 +77,62 @@ def plot_profit(args) -> None:
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# We need to use the same pairs, same tick_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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timerange = Arguments.parse_timerange(args.timerange)
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filter_pairs = args.pair
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config = misc.load_config(args.config)
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config.update({'strategy': args.strategy})
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config = Configuration(args).get_config()
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# Init strategy
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strategy = Strategy()
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strategy.init(config)
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try:
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analyze = Analyze({'strategy': config.get('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()
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# Take pairs from the cli otherwise switch to the pair in the config file
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if args.pair:
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filter_pairs = args.pair
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filter_pairs = filter_pairs.split(',')
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else:
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filter_pairs = config['exchange']['pair_whitelist']
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tick_interval = analyze.strategy.ticker_interval
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pairs = config['exchange']['pair_whitelist']
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if filter_pairs:
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filter_pairs = filter_pairs.split(',')
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pairs = list(set(pairs) & set(filter_pairs))
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print('Filter, keep pairs %s' % pairs)
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logger.info('Filter, keep pairs %s' % pairs)
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timerange = misc.parse_timerange(args.timerange)
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tickers = optimize.load_data(args.datadir, pairs=pairs,
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ticker_interval=strategy.ticker_interval,
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refresh_pairs=False,
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timerange=timerange)
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dataframes = optimize.preprocess(tickers)
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tickers = optimize.load_data(
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datadir=args.datadir,
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pairs=pairs,
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ticker_interval=tick_interval,
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refresh_pairs=False,
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timerange=timerange
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)
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dataframes = analyze.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 = misc.common_datearray(dataframes)
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max_x = dates.size
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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, tick_interval) + 1
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# Make an average close price of all the pairs that was 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(max_x)
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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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print('Pair %s has length %s' % (pair, len(close)))
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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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@ -114,10 +142,16 @@ def plot_profit(args) -> None:
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# Load the profits results
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# And make an profits-growth array
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filename = 'backtest-result.json'
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with open(filename) as file:
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data = json.load(file)
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pg = make_profit_array(data, max_x, filter_pairs)
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try:
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filename = 'backtest-result.json'
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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('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(0)
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pg = make_profit_array(data, num_iterations, min_date, tick_interval, filter_pairs)
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#
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# Plot the pairs average close prices, and total profit growth
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@ -128,6 +162,7 @@ def plot_profit(args) -> None:
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y=avgclose,
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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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@ -140,7 +175,7 @@ def plot_profit(args) -> 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, max_x, pair)
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pg = make_profit_array(data, num_iterations, min_date, tick_interval, pair)
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pair_profit = go.Scattergl(
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x=dates,
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y=pg,
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@ -151,6 +186,37 @@ def plot_profit(args) -> None:
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plot(fig, filename='freqtrade-profit-plot.html')
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def define_index(min_date, max_date, interval):
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"""
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Return the index of a specific date
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"""
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return int((max_date - min_date) / (interval * 60))
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def plot_parse_args(args):
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"""
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Parse args passed to the script
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:param args: Cli arguments
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:return: args: Array with all arguments
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"""
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arguments = Arguments(args, 'Graph profits')
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arguments.scripts_options()
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arguments.common_args_parser()
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arguments.optimizer_shared_options(arguments.parser)
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arguments.backtesting_options(arguments.parser)
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return arguments.parse_args()
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def main(sysargv: Dict) -> None:
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"""
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This function will initiate the bot and start the trading loop.
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:return: None
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"""
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logger.info('Starting Plot Dataframe')
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plot_profit(
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plot_parse_args(sysargv)
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)
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if __name__ == '__main__':
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args = plot_parse_args(sys.argv[1:])
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plot_profit(args)
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main(sys.argv[1:])
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