433 lines
11 KiB
Python
Executable File
433 lines
11 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Script to display when the bot will buy a specific pair
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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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-s / --strategy: strategy to use
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-d / --datadir: path to pair backtest data
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--timerange: specify what timerange of data to use.
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-l / --live: Live, to download the latest ticker for the pair
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"""
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import datetime
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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 typing import List
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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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import freqtrade.optimize as optimize
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from freqtrade import exchange
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from freqtrade.analyze import Analyze
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from freqtrade.arguments import Arguments
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from freqtrade.configuration import Configuration
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logger = logging.getLogger('freqtrade')
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def plot_dataframes_markers(data, fig, args):
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"""
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plots additional dataframe markers in the main plot
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:param data:
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:param fig:
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:param args:
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:return:
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"""
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if args.plotdataframemarker:
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for x in args.plotdataframemarker:
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filter = data[(data[x] == 100 ) | (data[x] == -100) ]
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marker = go.Scatter(
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x=filter.date,
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y=filter.low * 0.99,
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mode='markers',
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name=x,
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marker=dict(
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symbol='diamond-tall-open',
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size=10,
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line=dict(width=1)
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)
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)
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fig.append_trace(marker, 1, 1)
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def plot_dataframes(data, fig, args):
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"""
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plots additional dataframes in the main plot
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:param data:
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:param fig:
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:param args:
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:return:
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"""
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if args.plotdataframe:
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for x in args.plotdataframe:
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chart = go.Scattergl(x=data['date'], y=data[x], name=x)
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fig.append_trace(chart, 1, 1)
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def plot_volume_dataframe(data, fig, args, plotnumber):
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"""
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adds the plotting of the volume
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:param data:
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:param fig:
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:param args:
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:return:
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"""
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volume = go.Bar(x=data['date'], y=data['volume'], name='Volume')
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fig.append_trace(volume, plotnumber, 1)
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def plot_macd_dataframe(data, fig, args, plotnumber):
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"""
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adds the plotting of the MACD if specified
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:param data:
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:param fig:
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:param args:
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:return:
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"""
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macd = go.Scattergl(x=data['date'], y=data[args.plotmacd], name='MACD')
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macdsignal = go.Scattergl(x=data['date'], y=data[args.plotmacd + 'signal'], name='MACD signal')
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fig.append_trace(macd, plotnumber, 1)
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fig.append_trace(macdsignal, plotnumber, 1)
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def plot_rsi_dataframe(data, fig, args, plotnumber):
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"""
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this function plots an additional RSI chart under the exiting charts
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:param data:
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:param fig:
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:param args:
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:return:
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"""
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if args.plotrsi:
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for x in args.plotrsi:
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rsi = go.Scattergl(x=data['date'], y=data[x], name=x)
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fig.append_trace(rsi, plotnumber, 1)
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def plot_cci_dataframe(data, fig, args, plotnumber):
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"""
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this function plots an additional cci chart under the exiting charts
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:param data:
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:param fig:
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:param args:
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:return:
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"""
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if args.plotcci:
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for x in args.plotcci:
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chart = go.Scattergl(x=data['date'], y=data[x], name=x)
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fig.append_trace(chart, plotnumber, 1)
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def plot_stop_loss_trade(df_sell, fig, analyze, args):
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"""
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plots the stop loss for the associated trades and buys
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as well as the estimated profit ranges.
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will be enabled if --stop-loss is provided
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as argument
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:param data:
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:param trades:
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:return:
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"""
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if args.stoplossdisplay is False:
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return
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if 'associated_buy_price' not in df_sell:
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return
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stoploss = analyze.strategy.stoploss
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for index, x in df_sell.iterrows():
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if x['associated_buy_price'] > 0:
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# draw stop loss
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fig['layout']['shapes'].append(
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{
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'fillcolor': 'red',
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'opacity': 0.1,
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'type': 'rect',
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'x0': x['associated_buy_date'],
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'x1': x['date'],
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'y0': x['associated_buy_price'],
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'y1': (x['associated_buy_price'] - abs(stoploss) * x['associated_buy_price']),
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'line': {'color': 'red'}
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}
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)
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totalTime = 0
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for time in analyze.strategy.minimal_roi:
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t = int(time)
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totalTime = t + totalTime
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enddate = x['date']
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date = x['associated_buy_date'] + datetime.timedelta(minutes=totalTime)
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# draw profit range
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fig['layout']['shapes'].append(
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{
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'fillcolor': 'green',
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'opacity': 0.1,
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'type': 'rect',
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'x0': date,
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'x1': enddate,
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'y0': x['associated_buy_price'],
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'y1': x['associated_buy_price'] + x['associated_buy_price'] * analyze.strategy.minimal_roi[
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time],
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'line': {'color': 'green'}
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}
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)
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def find_profits(data):
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"""
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finds the profits between sells and the associated buys. This does not take in account
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ROI!
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:param data:
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:return:
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"""
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# go over all the sells
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# find all previous buys
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df_sell = data[data['sell'] == 1]
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df_sell['profit'] = 0
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df_buys = data[data['buy'] == 1]
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lastDate = data['date'].iloc[0]
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for index, row in df_sell.iterrows():
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buys = df_buys[(df_buys['date'] < row['date']) & (df_buys['date'] > lastDate)]
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profit = None
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if buys['date'].count() > 0:
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buys = buys.tail()
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profit = round(row['close'] / buys['close'].values[0] * 100 - 100, 2)
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lastDate = row['date']
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df_sell.loc[index, 'associated_buy_date'] = buys['date'].values[0]
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df_sell.loc[index, 'associated_buy_price'] = buys['close'].values[0]
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df_sell.loc[index, 'profit'] = profit
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return df_sell
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def plot_analyzed_dataframe(args: Namespace) -> None:
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"""
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Calls analyze() and plots the returned dataframe
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:return: None
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"""
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pair = args.pair.replace('-', '_')
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timerange = Arguments.parse_timerange(args.timerange)
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# Init strategy
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try:
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config = Configuration(args)
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analyze = Analyze(config.get_config())
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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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args.strategy
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)
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exit()
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tick_interval = analyze.strategy.ticker_interval
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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.init({'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(
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datadir=args.datadir,
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pairs=[pair],
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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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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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if len(dataframe.index) > args.plotticks:
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logger.warning('Ticker contained more than {} candles, clipping.'.format(args.plotticks))
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data = dataframe.tail(args.plotticks)
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candles = go.Candlestick(
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x=data.date,
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open=data.open,
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high=data.high,
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low=data.low,
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close=data.close,
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name='Price'
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)
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df_buy = data[data['buy'] == 1]
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buys = go.Scattergl(
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x=df_buy.date,
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y=df_buy.close * 0.995,
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mode='markers',
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name='buy',
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marker=dict(
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symbol='triangle-up-dot',
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size=15,
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line=dict(width=1),
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color='green',
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)
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)
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df_sell = find_profits(data)
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sells = go.Scatter(
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x=df_sell.date,
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y=df_sell.close * 1.01,
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mode='markers+text',
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name='sell',
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text=df_sell.profit,
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textposition='top right',
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marker=dict(
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symbol='triangle-down-dot',
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size=15,
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line=dict(width=1),
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color='red',
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)
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)
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bb_lower = go.Scatter(
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x=data.date,
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y=data.bb_lowerband,
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name='BB lower',
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line={'color': "transparent"},
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)
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bb_upper = go.Scatter(
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x=data.date,
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y=data.bb_upperband,
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name='BB upper',
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fill="tonexty",
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fillcolor="rgba(0,176,246,0.2)",
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line={'color': "transparent"},
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)
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bb_middle = go.Scatter(
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x=data.date,
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y=data.bb_middleband,
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name='BB middle',
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fill="tonexty",
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fillcolor="rgba(0,176,246,0.2)",
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line={'color': "red"},
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)
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# ugly hack for now
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rowWidth = [1]
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if args.plotvolume:
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rowWidth.append(1)
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if args.plotmacd:
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rowWidth.append(1)
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if args.plotrsi:
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rowWidth.append(1)
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if args.plotcci:
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rowWidth.append(1)
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# standard layout signal + volume
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fig = tools.make_subplots(
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rows=len(rowWidth),
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cols=1,
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shared_xaxes=True,
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row_width=rowWidth,
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vertical_spacing=0.0001,
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)
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# todo should be optional
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fig.append_trace(candles, 1, 1)
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fig.append_trace(bb_lower, 1, 1)
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fig.append_trace(bb_middle, 1, 1)
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fig.append_trace(bb_upper, 1, 1)
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fig.append_trace(buys, 1, 1)
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fig.append_trace(sells, 1, 1)
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# append stop loss/profit
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plot_stop_loss_trade(df_sell, fig, analyze, args)
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# plot other dataframes
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plot_dataframes(data, fig, args)
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plot_dataframes_markers(data, fig, args)
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fig['layout'].update(title=args.pair)
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fig['layout']['yaxis1'].update(title='Price')
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subplots = 1
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if args.plotvolume:
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subplots = subplots + 1
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plot_volume_dataframe(data, fig, args, subplots)
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fig['layout']['yaxis' + str(subplots)].update(title='Volume')
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if args.plotmacd:
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subplots = subplots + 1
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plot_macd_dataframe(data, fig, args, subplots)
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fig['layout']['yaxis' + str(subplots)].update(title='MACD')
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if args.plotrsi:
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subplots = subplots + 1
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plot_rsi_dataframe(data, fig, args, subplots)
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fig['layout']['yaxis' + str(subplots)].update(title='RSI', range=[0, 100])
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if args.plotcci:
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subplots = subplots + 1
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plot_cci_dataframe(data, fig, args, subplots)
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fig['layout']['yaxis' + str(subplots)].update(title='CCI')
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# updated all the
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plot(fig, filename='freqtrade-plot.html')
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def plot_parse_args(args: List[str]) -> Namespace:
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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 dataframe')
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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: List[str]) -> 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_analyzed_dataframe(
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plot_parse_args(sysargv)
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)
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if __name__ == '__main__':
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main(sys.argv[1:])
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