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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()