Janne Sinivirta
GitHub
commit
022fedb5d2
@ -17,6 +17,19 @@ python script/plot_dataframe.py -p BTC_ETH,BTC_LTC
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The -p pair argument, can be used to specify what
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pair you would like to plot.
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**Advanced use**
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To plot the current live price use the --live flag:
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```
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python scripts/plot_dataframe.py -p BTC_ETH --live
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```
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To plot a timerange (to zoom in):
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```
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python scripts/plot_dataframe.py -p BTC_ETH --timerange=100-200
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```
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Timerange doesn't work with live data.
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## Plot profit
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@ -46,3 +59,11 @@ Example
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```
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python python scripts/plot_profit.py --datadir ../freqtrade/freqtrade/tests/testdata-20171221/ -p BTC_LTC
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```
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**When it goes wrong**
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*** Linux: Can't display**
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If you are inside an python environment, you might want to set the
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DISPLAY variable as so:
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$ DISPLAY=:0 python scripts/plot_dataframe.py
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@ -5,8 +5,10 @@ import logging
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import time
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import os
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import re
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from datetime import datetime
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from typing import Any, Callable, Dict, List
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import numpy as np
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from jsonschema import Draft4Validator, validate
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from jsonschema.exceptions import ValidationError, best_match
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from wrapt import synchronized
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@ -16,11 +18,6 @@ from freqtrade import __version__
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logger = logging.getLogger(__name__)
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def file_dump_json(filename, data):
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with open(filename, 'w') as fp:
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json.dump(data, fp)
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class State(enum.Enum):
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RUNNING = 0
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STOPPED = 1
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@ -30,6 +27,44 @@ class State(enum.Enum):
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_STATE = State.STOPPED
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############################################
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# Used by scripts #
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# Matplotlib doesn't support ::datetime64, #
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# so we need to convert it into ::datetime #
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############################################
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def datesarray_to_datetimearray(dates):
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"""
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Convert an pandas-array of timestamps into
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An numpy-array of datetimes
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:return: numpy-array of datetime
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"""
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times = []
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dates = dates.astype(datetime)
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for i in range(0, dates.size):
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date = dates[i].to_pydatetime()
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times.append(date)
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return np.array(times)
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def common_datearray(dfs):
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alldates = {}
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for pair, pair_data in dfs.items():
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dates = datesarray_to_datetimearray(pair_data['date'])
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for date in dates:
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alldates[date] = 1
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lst = []
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for date, _ in alldates.items():
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lst.append(date)
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arr = np.array(lst)
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return np.sort(arr, axis=0)
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def file_dump_json(filename, data):
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with open(filename, 'w') as fp:
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json.dump(data, fp)
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@synchronized
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def update_state(state: State) -> None:
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"""
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@ -163,6 +198,15 @@ def parse_args(args: List[str], description: str):
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return parser.parse_args(args)
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def scripts_options(parser: argparse.ArgumentParser) -> None:
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parser.add_argument(
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'-p', '--pair',
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help='Show profits for only this pairs. Pairs are comma-separated.',
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dest='pair',
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default=None
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)
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def backtesting_options(parser: argparse.ArgumentParser) -> None:
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parser.add_argument(
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'-l', '--live',
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@ -1,34 +1,32 @@
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#!/usr/bin/env python3
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import sys
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import logging
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import argparse
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import matplotlib
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# matplotlib.use("Qt5Agg")
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import matplotlib.dates as mdates
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import matplotlib.pyplot as plt
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from pandas import DataFrame
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade import exchange, analyze
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from freqtrade.misc import common_args_parser
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from freqtrade.strategy.strategy import Strategy
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import matplotlib.pyplot as plt
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import matplotlib # Install PYQT5 manually if you want to test this helper function
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import freqtrade.misc as misc
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import freqtrade.optimize as optimize
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import freqtrade.analyze as analyze
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matplotlib.use("Qt5Agg")
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logger = logging.getLogger(__name__)
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def plot_parse_args(args):
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parser = common_args_parser(description='Graph utility')
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parser.add_argument(
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'-p', '--pair',
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help='What currency pair',
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dest='pair',
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default='BTC_ETH',
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type=str,
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)
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parser.add_argument(
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'-i', '--interval',
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help='what interval to use',
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dest='interval',
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default=5,
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type=int,
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)
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parser = misc.common_args_parser('Graph dataframe')
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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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@ -38,80 +36,65 @@ def plot_analyzed_dataframe(args) -> None:
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:param pair: pair as str
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:return: None
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"""
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pair = args.pair
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pairs = [pair]
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timerange = misc.parse_timerange(args.timerange)
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# Init strategy
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strategy = Strategy()
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strategy.init({'strategy': args.strategy})
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tick_interval = strategy.ticker_interval
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# Init Bittrex to use public API
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exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
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ticker = exchange.get_ticker_history(args.pair, args.interval)
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dataframe = analyze.analyze_ticker(ticker)
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dataframe = populate_indicator(dataframe)
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tickers = {}
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if args.live:
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logger.info('Downloading pair.')
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# Init Bittrex to use public API
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exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
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tickers[pair] = exchange.get_ticker_history(pair, tick_interval)
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else:
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tickers = optimize.load_data(args.datadir, 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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dataframes = optimize.tickerdata_to_dataframe(tickers)
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dataframe = dataframes[pair]
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dataframe = analyze.populate_buy_trend(dataframe)
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dataframe = analyze.populate_sell_trend(dataframe)
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dates = misc.datesarray_to_datetimearray(dataframe['date'])
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# Two subplots sharing x axis
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fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
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fig.suptitle(args.pair + " " + str(args.interval), fontsize=14, fontweight='bold')
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ax1.plot(dataframe.index.values, dataframe['close'], label='close')
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# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
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ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
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ax1.plot(dataframe.index.values, dataframe['tema'], ':', label='TEMA')
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ax1.plot(dataframe.index.values, dataframe['bb_lowerband'], '-.', label='BB low')
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ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
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fig.suptitle(pair + " " + str(tick_interval), fontsize=14, fontweight='bold')
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ax1.plot(dates, dataframe['close'], label='close')
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# ax1.plot(dates, dataframe['sell'], 'ro', label='sell')
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ax1.plot(dates, dataframe['sma'], '--', label='SMA')
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ax1.plot(dates, dataframe['tema'], ':', label='TEMA')
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ax1.plot(dates, dataframe['blower'], '-.', label='BB low')
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ax1.plot(dates, dataframe['close'] * dataframe['buy'], 'bo', label='buy')
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ax1.plot(dates, dataframe['close'] * dataframe['sell'], 'ro', label='sell')
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ax1.legend()
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ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
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ax2.plot(dataframe.index.values, dataframe['mfi'], label='MFI')
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# ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
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ax2.plot(dates, dataframe['adx'], label='ADX')
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ax2.plot(dates, dataframe['mfi'], label='MFI')
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# ax2.plot(dates, [25] * len(dataframe.index.values))
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ax2.legend()
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ax3.plot(dataframe.index.values, dataframe['fastk'], label='k')
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ax3.plot(dataframe.index.values, dataframe['fastd'], label='d')
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ax3.plot(dataframe.index.values, [20] * len(dataframe.index.values))
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ax3.plot(dates, dataframe['fastk'], label='k')
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ax3.plot(dates, dataframe['fastd'], label='d')
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ax3.plot(dates, [20] * len(dataframe.index.values))
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ax3.legend()
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xfmt = mdates.DateFormatter('%d-%m-%y %H:%M') # Dont let matplotlib autoformat date
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ax3.xaxis.set_major_formatter(xfmt)
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# Fine-tune figure; make subplots close to each other and hide x ticks for
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# all but bottom plot.
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fig.subplots_adjust(hspace=0)
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fig.autofmt_xdate() # Rotate the dates
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plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
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plt.show()
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def populate_indicator(dataframe: DataFrame) -> DataFrame:
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dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
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dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
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# ADX
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if 'adx' not in dataframe:
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dataframe['adx'] = ta.ADX(dataframe)
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# Bollinger bands
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if 'bb_lowerband' not in dataframe:
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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# Stoch fast
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if 'fastd' not in dataframe or 'fastk' not in dataframe:
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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# MFI
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if 'mfi' not in dataframe:
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dataframe['mfi'] = ta.MFI(dataframe)
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# SMA - Simple Moving Average
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if 'sma' not in dataframe:
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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# TEMA - Triple Exponential Moving Average
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if 'tema' not in dataframe:
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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return dataframe
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if __name__ == '__main__':
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args = plot_parse_args(sys.argv[1:])
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plot_analyzed_dataframe(args)
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@ -3,23 +3,20 @@
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import sys
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import json
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import numpy as np
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import freqtrade.optimize as optimize
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import freqtrade.misc as misc
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import freqtrade.exchange as exchange
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from freqtrade.strategy.strategy import Strategy
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def plot_parse_args(args):
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parser = misc.common_args_parser('Graph utility')
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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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parser.add_argument(
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'-p', '--pair',
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help='Show profits for only this pairs. Pairs are comma-separated.',
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dest='pair',
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default=None
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)
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misc.scripts_options(parser)
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return parser.parse_args(args)
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@ -39,7 +36,9 @@ def make_profit_array(data, px, filter_pairs=[]):
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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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pg[tim+dur-1] += profit
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ix = tim + dur - 1
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if ix < px:
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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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@ -81,28 +80,21 @@ def plot_profit(args) -> None:
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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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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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refresh_pairs=False,
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timerange=timerange)
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dataframes = optimize.preprocess(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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# 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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# FIX: since the dataframes are of unequal length,
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# andor has different dates, we need to merge them
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# But we dont have the date information in the
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# backtesting results, this is needed to match the dates
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# For now, assume the dataframes are aligned.
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max_x = 0
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for pair, pair_data in dataframes.items():
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n = len(pair_data['close'])
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max_x = max(max_x, n)
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# if max_x != n:
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# raise Exception('Please rerun script. Input data has different lengths %s'
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# %('Different pair length: %s <=> %s' %(max_x, n)))
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print('max_x: %s' % (max_x))
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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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@ -132,8 +124,9 @@ def plot_profit(args) -> None:
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fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
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fig.suptitle('total profit')
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ax1.plot(avgclose, label='avgclose')
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ax2.plot(pg, label='profit')
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ax1.plot(dates, avgclose, label='avgclose')
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ax2.plot(dates, pg, label='profit')
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ax1.legend(loc='upper left')
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ax2.legend(loc='upper left')
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@ -143,15 +136,15 @@ def plot_profit(args) -> None:
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# In third graph, we plot each profit separately
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for pair in pairs:
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pg = make_profit_array(data, max_x, pair)
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ax3.plot(pg, label=pair)
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ax3.plot(dates, pg, label=pair)
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ax3.legend(loc='upper left')
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# black background to easier see multiple colors
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ax3.set_facecolor('black')
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xfmt = mdates.DateFormatter('%d-%m-%y %H:%M') # Dont let matplotlib autoformat date
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ax3.xaxis.set_major_formatter(xfmt)
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# Fine-tune figure; make subplots close to each other and hide x ticks for
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# all but bottom plot.
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fig.subplots_adjust(hspace=0)
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plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
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fig.autofmt_xdate() # Rotate the dates
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plt.show()
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Loading…
Reference in New Issue
Block a user