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Use dates on plot profit/dataframe

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* plot_dataframe also support --timerange
* Both default to tkinter as matplotlib plotting backend
This commit is contained in:
kryofly
2018-01-21 13:44:30 +01:00
parent f40d9dbb05
commit 6171be4f46
4 changed files with 132 additions and 63 deletions
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73
scripts/plot_dataframe.py
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@@ -1,23 +1,23 @@
#!/usr/bin/env python3
import sys
import logging
import argparse
import matplotlib # Install PYQT5 manually if you want to test this helper function
matplotlib.use("Qt5Agg")
import matplotlib
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
from freqtrade import exchange, analyze
from freqtrade.misc import common_args_parser
import freqtrade.misc as misc
import freqtrade.optimize as optimize
import freqtrade.analyze as analyze
logger = logging.getLogger(__name__)
def plot_parse_args(args ):
parser = common_args_parser(description='Graph utility')
parser.add_argument(
'-p', '--pair',
help = 'What currency pair',
dest = 'pair',
default = 'BTC_ETH',
type = str,
)
def plot_parse_args(args):
parser = misc.common_args_parser('Graph dataframe')
misc.backtesting_options(parser)
misc.scripts_options(parser)
return parser.parse_args(args)
@@ -28,11 +28,25 @@ def plot_analyzed_dataframe(args):
:return: None
"""
pair = args.pair
pairs = [pair]
timerange = misc.parse_timerange(args.timerange)
# Init Bittrex to use public API
exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
ticker = exchange.get_ticker_history(pair)
dataframe = analyze.analyze_ticker(ticker)
tickers = {}
if args.live:
logger.info('Downloading pair.')
exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
tickers[pair] = exchange.get_ticker_history(pair, args.ticker_interval)
else:
tickers = optimize.load_data(args.datadir, pairs=pairs,
ticker_interval=args.ticker_interval,
refresh_pairs=False,
timerange=timerange)
dataframes = optimize.tickerdata_to_dataframe(tickers)
dataframe = dataframes[pair]
dataframe = analyze.populate_buy_trend(dataframe)
dataframe = analyze.populate_sell_trend(dataframe)
dates = misc.datesarray_to_datetimearray(dataframe['date'])
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
@@ -40,27 +54,30 @@ def plot_analyzed_dataframe(args):
# Two subplots sharing x axis
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.suptitle(pair, fontsize=14, fontweight='bold')
ax1.plot(dataframe.index.values, dataframe['close'], label='close')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
ax1.plot(dataframe.index.values, dataframe['tema'], ':', label='TEMA')
ax1.plot(dataframe.index.values, dataframe['blower'], '-.', label='BB low')
ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
ax1.plot(dates, dataframe['close'], label='close')
# ax1.plot(dates, dataframe['sell'], 'ro', label='sell')
ax1.plot(dates, dataframe['sma'], '--', label='SMA')
ax1.plot(dates, dataframe['tema'], ':', label='TEMA')
ax1.plot(dates, dataframe['blower'], '-.', label='BB low')
ax1.plot(dates, dataframe['buy_price'], 'bo', label='buy')
ax1.legend()
ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
ax2.plot(dataframe.index.values, dataframe['mfi'], label='MFI')
# ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
ax2.plot(dates, dataframe['adx'], label='ADX')
ax2.plot(dates, dataframe['mfi'], label='MFI')
# ax2.plot(dates, [25] * len(dataframe.index.values))
ax2.legend()
ax3.plot(dataframe.index.values, dataframe['fastk'], label='k')
ax3.plot(dataframe.index.values, dataframe['fastd'], label='d')
ax3.plot(dataframe.index.values, [20] * len(dataframe.index.values))
ax3.plot(dates, dataframe['fastk'], label='k')
ax3.plot(dates, dataframe['fastd'], label='d')
ax3.plot(dates, [20] * len(dataframe.index.values))
ax3.legend()
xfmt = mdates.DateFormatter('%d-%m-%y %H:%M') # Dont let matplotlib autoformat date
ax3.xaxis.set_major_formatter(xfmt)
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
fig.subplots_adjust(hspace=0)
fig.autofmt_xdate() # Rotate the dates
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
plt.show()

47
scripts/plot_profit.py
View File

@@ -4,24 +4,19 @@ import sys
import argparse
import json
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import freqtrade.optimize as optimize
import freqtrade.misc as misc
import freqtrade.exchange as exchange
import freqtrade.analyze as analyze
def plot_parse_args(args ):
parser = misc.common_args_parser('Graph utility')
def plot_parse_args(args):
parser = misc.common_args_parser('Graph profits')
# FIX: perhaps delete those backtesting options that are not feasible (shows up in -h)
misc.backtesting_options(parser)
parser.add_argument(
'-p', '--pair',
help = 'Show profits for only this pairs. Pairs are comma-separated.',
dest = 'pair',
default = None
)
misc.scripts_options(parser)
return parser.parse_args(args)
@@ -85,23 +80,14 @@ def plot_profit(args) -> None:
timerange=timerange)
dataframes = optimize.preprocess(tickers)
# NOTE: the dataframes are of unequal length,
# 'dates' is an merged date array of them all.
dates = misc.common_datearray(dataframes)
max_x = dates.size
# Make an average close price of all the pairs that was involved.
# this could be useful to gauge the overall market trend
# FIX: since the dataframes are of unequal length,
# andor has different dates, we need to merge them
# But we dont have the date information in the
# backtesting results, this is needed to match the dates
# For now, assume the dataframes are aligned.
max_x = 0
for pair, pair_data in dataframes.items():
n = len(pair_data['close'])
max_x = max(max_x, n)
# if max_x != n:
# raise Exception('Please rerun script. Input data has different lengths %s'
# %('Different pair length: %s <=> %s' %(max_x, n)))
print('max_x: %s' %(max_x))
# We are essentially saying:
# array <- sum dataframes[*]['close'] / num_items dataframes
# FIX: there should be some onliner numpy/panda for this
@@ -131,8 +117,9 @@ def plot_profit(args) -> None:
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.suptitle('total profit')
ax1.plot(avgclose, label='avgclose')
ax2.plot(pg, label='profit')
ax1.plot(dates, avgclose, label='avgclose')
ax2.plot(dates, pg, label='profit')
ax1.legend(loc='upper left')
ax2.legend(loc='upper left')
@@ -142,15 +129,15 @@ def plot_profit(args) -> None:
# In third graph, we plot each profit separately
for pair in pairs:
pg = make_profit_array(data, max_x, pair)
ax3.plot(pg, label=pair)
ax3.plot(dates, pg, label=pair)
ax3.legend(loc='upper left')
# black background to easier see multiple colors
ax3.set_facecolor('black')
xfmt = mdates.DateFormatter('%d-%m-%y %H:%M') # Dont let matplotlib autoformat date
ax3.xaxis.set_major_formatter(xfmt)
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
fig.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
fig.autofmt_xdate() # Rotate the dates
plt.show()