464 lines
14 KiB
Python
Executable File
464 lines
14 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Script to display when the bot will buy on specific pair(s)
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Mandatory Cli parameters:
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-p / --pairs: pair(s) 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(s) 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(s)
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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 --pairs BTC/EUR,XRP/BTC -d user_data/data/
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--indicators1 sma,ema3 --indicators2 fastk,fastd
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"""
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import json
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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 pathlib import Path
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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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import pytz
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from plotly import tools
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from plotly.offline import plot
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from freqtrade import persistence
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from freqtrade.arguments import Arguments, TimeRange
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from freqtrade.data import history
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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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from freqtrade.resolvers import StrategyResolver
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logger = logging.getLogger(__name__)
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_CONF: Dict[str, Any] = {}
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timeZone = pytz.UTC
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def load_trades(args: Namespace, pair: str, timerange: TimeRange) -> pd.DataFrame:
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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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columns = ["pair", "profit", "opents", "closets", "open_rate", "close_rate", "duration"]
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for x in Trade.query.all():
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print("date: {}".format(x.open_date))
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trades = pd.DataFrame([(t.pair, t.calc_profit(),
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t.open_date.replace(tzinfo=timeZone),
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t.close_date.replace(tzinfo=timeZone) if t.close_date else None,
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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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if t.close_date else None)
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for t in Trade.query.filter(Trade.pair.is_(pair)).all()],
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columns=columns)
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elif 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", "sell_reason"]
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if file.exists():
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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(
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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(
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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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else:
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trades = pd.DataFrame([], columns=columns)
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return trades
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def generate_plot_file(fig, pair, tick_interval, is_last) -> None:
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"""
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Generate a plot html file from pre populated fig plotly object
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:return: None
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"""
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logger.info('Generate plot file for %s', pair)
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pair_name = pair.replace("/", "_")
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file_name = 'freqtrade-plot-' + pair_name + '-' + tick_interval + '.html'
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Path("user_data/plots").mkdir(parents=True, exist_ok=True)
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plot(fig, filename=str(Path('user_data/plots').joinpath(file_name)), auto_open=False)
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if is_last:
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plot(fig, filename=str(Path('user_data').joinpath('freqtrade-plot.html')), auto_open=False)
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def get_trading_env(args: Namespace):
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"""
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Initalize freqtrade Exchange and Strategy, split pairs recieved in parameter
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:return: Strategy
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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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print(_CONF)
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pairs = args.pairs.split(',')
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if pairs is None:
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logger.critical('Parameter --pairs mandatory;. E.g --pairs ETH/BTC,XRP/BTC')
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exit()
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# Load the strategy
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try:
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strategy = StrategyResolver(_CONF).strategy
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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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return [strategy, exchange, pairs]
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def get_tickers_data(strategy, exchange, pairs: List[str], args):
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"""
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Get tickers data for each pairs on live or local, option defined in args
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:return: dictinnary of tickers. output format: {'pair': tickersdata}
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"""
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tick_interval = strategy.ticker_interval
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timerange = Arguments.parse_timerange(args.timerange)
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tickers = {}
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if args.live:
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logger.info('Downloading pairs.')
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exchange.refresh_latest_ohlcv([(pair, tick_interval) for pair in pairs])
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for pair in pairs:
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tickers[pair] = exchange.klines((pair, tick_interval))
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else:
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tickers = history.load_data(
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datadir=Path(_CONF.get("datadir")),
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pairs=pairs,
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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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exchange=Exchange(_CONF)
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)
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# No ticker found, impossible to download, len mismatch
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for pair, data in tickers.copy().items():
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logger.debug("checking tickers data of pair: %s", pair)
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logger.debug("data.empty: %s", data.empty)
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logger.debug("len(data): %s", len(data))
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if data.empty:
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del tickers[pair]
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logger.info(
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'An issue occured while retreiving datas of %s pair, please retry '
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'using -l option for live or --refresh-pairs-cached', pair)
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return tickers
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def generate_dataframe(strategy, tickers, pair) -> pd.DataFrame:
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"""
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Get tickers then Populate strategy indicators and signals, then return the full dataframe
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:return: the DataFrame of a pair
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"""
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dataframes = strategy.tickerdata_to_dataframe(tickers)
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dataframe = dataframes[pair]
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dataframe = strategy.advise_buy(dataframe, {'pair': pair})
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dataframe = strategy.advise_sell(dataframe, {'pair': pair})
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return dataframe
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def extract_trades_of_period(dataframe, trades) -> pd.DataFrame:
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"""
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Compare trades and backtested pair DataFrames to get trades performed on backtested period
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:return: the DataFrame of a trades of period
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"""
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trades = trades.loc[trades['opents'] >= dataframe.iloc[0]['date']]
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return trades
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def generate_graph(
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pair: str,
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trades: pd.DataFrame,
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data: pd.DataFrame,
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indicators1: str,
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indicators2: str
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) -> 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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:indicators1: String Main plot indicators
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:indicators2: String Sub plot indicators
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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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fig['layout']['xaxis']['rangeslider'].update(visible=False)
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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': 'rgba(255,255,255,0)'},
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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': 'rgba(255,255,255,0)'},
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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=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=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,macdsignal',
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dest='indicators2',
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)
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arguments.parser.add_argument(
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'--plot-limit',
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help='Specify tick limit for plotting - too high values cause huge files - '
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'Default: %(default)s',
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dest='plot_limit',
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default=750,
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type=int,
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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 analyse_and_plot_pairs(args: Namespace):
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"""
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From arguments provided in cli:
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-Initialise backtest env
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-Get tickers data
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-Generate Dafaframes populated with indicators and signals
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-Load trades excecuted on same periods
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-Generate Plotly plot objects
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-Generate plot files
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:return: None
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"""
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strategy, exchange, pairs = get_trading_env(args)
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# Set timerange to use
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timerange = Arguments.parse_timerange(args.timerange)
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tick_interval = strategy.ticker_interval
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tickers = get_tickers_data(strategy, exchange, pairs, args)
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pair_counter = 0
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for pair, data in tickers.items():
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pair_counter += 1
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logger.info("analyse pair %s", pair)
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tickers = {}
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tickers[pair] = data
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dataframe = generate_dataframe(strategy, tickers, pair)
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trades = load_trades(args, pair, timerange)
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trades = extract_trades_of_period(dataframe, trades)
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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,
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indicators1=args.indicators1,
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indicators2=args.indicators2
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)
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is_last = (False, True)[pair_counter == len(tickers)]
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generate_plot_file(fig, pair, tick_interval, is_last)
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logger.info('End of ploting process %s plots generated', pair_counter)
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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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analyse_and_plot_pairs(
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plot_parse_args(sysargv)
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)
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exit()
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if __name__ == '__main__':
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main(sys.argv[1:])
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