358 lines
10 KiB
Python
Executable File
358 lines
10 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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Option but recommended
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-s / --strategy: strategy to use
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Optional Cli parameters
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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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-db / --db-url: Show trades stored in database
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Indicators recommended
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Row 1: sma, ema3, ema5, ema10, ema50
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Row 3: macd, rsi, fisher_rsi, mfi, slowd, slowk, fastd, fastk
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Example of usage:
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> python3 scripts/plot_dataframe.py --pair BTC/EUR -d user_data/data/ --indicators1 sma,ema3
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--indicators2 fastk,fastd
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"""
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import logging
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import os
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import sys
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import json
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from pathlib import Path
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from argparse import Namespace
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from typing import Dict, List, Any
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import pandas as pd
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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 persistence
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from freqtrade.analyze import Analyze
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from freqtrade.arguments import Arguments
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from freqtrade.exchange import Exchange
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from freqtrade.optimize.backtesting import setup_configuration
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from freqtrade.persistence import Trade
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logger = logging.getLogger(__name__)
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_CONF: Dict[str, Any] = {}
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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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global _CONF
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# Load the configuration
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_CONF.update(setup_configuration(args))
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# Set the pair to audit
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pair = args.pair
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if pair is None:
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logger.critical('Parameter --pair mandatory;. E.g --pair ETH/BTC')
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exit()
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if '/' not in pair:
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logger.critical('--pair format must be XXX/YYY')
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exit()
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# Set timerange to use
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timerange = Arguments.parse_timerange(args.timerange)
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# Load the strategy
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try:
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analyze = Analyze(_CONF)
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exchange = Exchange(_CONF)
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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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# Set the ticker to use
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tick_interval = analyze.get_ticker_interval()
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# Load pair tickers
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tickers = {}
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if args.live:
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logger.info('Downloading pair.')
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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=_CONF.get("datadir"),
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pairs=[pair],
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ticker_interval=tick_interval,
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refresh_pairs=_CONF.get('refresh_pairs', False),
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timerange=timerange
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)
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# No ticker found, or impossible to download
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if tickers == {}:
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exit()
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# Get trades already made from the DB
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trades: pd.DataFrame = pd.DataFrame()
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if args.db_url:
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persistence.init(_CONF)
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trades_ = Trade.query.filter(Trade.pair.is_(pair)).all()
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# columns = ["pair", "profit", "opents", "closets", "index", "duration"]
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columns = ["pair", "profit", "opents", "closets", "open_rate", "close_rate", "duration"]
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trades = pd.DataFrame([(t.pair, t.calc_profit(),
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t.open_date, t.close_date,
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t.open_rate, t.close_rate,
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t.close_date.timestamp() - t.open_date.timestamp())
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for t in trades_], columns=columns)
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if args.exportfilename:
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file = Path(args.exportfilename)
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# must align with columns in backtest.py
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columns = ["pair", "profit", "opents", "closets", "index", "duration",
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"open_rate", "close_rate", "open_at_end"]
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with file.open() as f:
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data = json.load(f)
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trades = pd.DataFrame(data, columns=columns)
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trades = trades.loc[trades["pair"] == pair]
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if timerange:
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if timerange.starttype == 'date':
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trades = trades.loc[trades["opents"] >= timerange.startts]
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if timerange.stoptype == 'date':
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trades = trades.loc[trades["opents"] <= timerange.stopts]
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trades['opents'] = pd.to_datetime(trades['opents'],
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unit='s',
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utc=True,
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infer_datetime_format=True)
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trades['closets'] = pd.to_datetime(trades['closets'],
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unit='s',
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utc=True,
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infer_datetime_format=True)
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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) > 750:
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logger.warning('Ticker contained more than 750 candles, clipping.')
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fig = generate_graph(
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pair=pair,
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trades=trades,
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data=dataframe.tail(750),
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args=args
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)
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plot(fig, filename=os.path.join('user_data', 'freqtrade-plot.html'))
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def generate_graph(pair, trades: pd.DataFrame, data: pd.DataFrame, args) -> tools.make_subplots:
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"""
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Generate the graph from the data generated by Backtesting or from DB
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:param pair: Pair to Display on the graph
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:param trades: All trades created
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:param data: Dataframe
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:param args: sys.argv that contrains the two params indicators1, and indicators2
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:return: None
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"""
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# Define the graph
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fig = tools.make_subplots(
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rows=3,
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cols=1,
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shared_xaxes=True,
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row_width=[1, 1, 4],
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vertical_spacing=0.0001,
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)
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fig['layout'].update(title=pair)
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fig['layout']['yaxis1'].update(title='Price')
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fig['layout']['yaxis2'].update(title='Volume')
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fig['layout']['yaxis3'].update(title='Other')
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# Common information
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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,
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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=9,
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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 = data[data['sell'] == 1]
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sells = go.Scattergl(
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x=df_sell.date,
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y=df_sell.close,
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mode='markers',
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name='sell',
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marker=dict(
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symbol='triangle-down-dot',
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size=9,
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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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trade_buys = go.Scattergl(
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x=trades["opents"],
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y=trades["open_rate"],
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mode='markers',
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name='trade_buy',
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marker=dict(
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symbol='square-open',
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size=11,
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line=dict(width=2),
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color='green'
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)
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)
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trade_sells = go.Scattergl(
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x=trades["closets"],
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y=trades["close_rate"],
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mode='markers',
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name='trade_sell',
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marker=dict(
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symbol='square-open',
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size=11,
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line=dict(width=2),
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color='red'
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)
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)
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# Row 1
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fig.append_trace(candles, 1, 1)
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if 'bb_lowerband' in data and 'bb_upperband' in data:
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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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fig.append_trace(bb_lower, 1, 1)
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fig.append_trace(bb_upper, 1, 1)
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fig = generate_row(fig=fig, row=1, raw_indicators=args.indicators1, data=data)
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fig.append_trace(buys, 1, 1)
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fig.append_trace(sells, 1, 1)
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fig.append_trace(trade_buys, 1, 1)
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fig.append_trace(trade_sells, 1, 1)
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# Row 2
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volume = go.Bar(
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x=data['date'],
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y=data['volume'],
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name='Volume'
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)
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fig.append_trace(volume, 2, 1)
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# Row 3
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fig = generate_row(fig=fig, row=3, raw_indicators=args.indicators2, data=data)
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return fig
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def generate_row(fig, row, raw_indicators, data) -> tools.make_subplots:
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"""
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Generator all the indicator selected by the user for a specific row
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"""
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for indicator in raw_indicators.split(','):
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if indicator in data:
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scattergl = go.Scattergl(
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x=data['date'],
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y=data[indicator],
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name=indicator
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)
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fig.append_trace(scattergl, row, 1)
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else:
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logger.info(
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'Indicator "%s" ignored. Reason: This indicator is not found '
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'in your strategy.',
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indicator
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)
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return fig
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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.parser.add_argument(
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'--indicators1',
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help='Set indicators from your strategy you want in the first row of the graph. Separate '
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'them with a coma. E.g: ema3,ema5 (default: %(default)s)',
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type=str,
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default='sma,ema3,ema5',
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dest='indicators1',
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)
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arguments.parser.add_argument(
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'--indicators2',
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help='Set indicators from your strategy you want in the third row of the graph. Separate '
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'them with a coma. E.g: fastd,fastk (default: %(default)s)',
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type=str,
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default='macd',
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dest='indicators2',
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)
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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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