Merge pull request #1513 from Axel-CH/feature/plot_df_refactoring_multiple_pairs
Feature/plot df refactoring multiple pairs
This commit is contained in:
Matthias
GitHub
commit
3bc96c16ac
@ -15,7 +15,7 @@ At least version 2.3.0 is required.
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Usage for the price plotter:
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```
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script/plot_dataframe.py [-h] [-p pair] [--live]
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script/plot_dataframe.py [-h] [-p pairs] [--live]
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```
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Example
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@ -23,11 +23,16 @@ Example
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python scripts/plot_dataframe.py -p BTC/ETH
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```
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The `-p` pair argument, can be used to specify what
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pair you would like to plot.
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The `-p` pairs argument, can be used to specify
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pairs you would like to plot.
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**Advanced use**
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To plot multiple pairs, separate them with a comma:
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```
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python scripts/plot_dataframe.py -p BTC/ETH,XRP/ETH
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```
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To plot the current live price use the `--live` flag:
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```
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python scripts/plot_dataframe.py -p BTC/ETH --live
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@ -352,9 +352,9 @@ class Arguments(object):
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Parses given arguments for scripts.
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"""
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self.parser.add_argument(
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'-p', '--pair',
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'-p', '--pairs',
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help='Show profits for only this pairs. Pairs are comma-separated.',
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dest='pair',
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dest='pairs',
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default=None
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)
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@ -47,7 +47,7 @@ def test_scripts_options() -> None:
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arguments = Arguments(['-p', 'ETH/BTC'], '')
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arguments.scripts_options()
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args = arguments.get_parsed_arg()
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assert args.pair == 'ETH/BTC'
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assert args.pairs == 'ETH/BTC'
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def test_parse_args_version() -> None:
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@ -1,18 +1,18 @@
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#!/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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Script to display when the bot will buy on specific pair(s)
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Mandatory Cli parameters:
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-p / --pair: pair to examine
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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 backtest data
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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
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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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@ -21,8 +21,8 @@ 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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> 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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@ -65,7 +65,8 @@ def load_trades(args: Namespace, pair: str, timerange: TimeRange) -> pd.DataFram
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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() if t.close_date else None)
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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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@ -74,52 +75,66 @@ def load_trades(args: Namespace, pair: str, timerange: TimeRange) -> pd.DataFram
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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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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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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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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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return trades
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def plot_analyzed_dataframe(args: Namespace) -> None:
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def generate_plot_file(fig, pair, tick_interval, is_last) -> None:
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"""
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Calls analyze() and plots the returned dataframe
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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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# 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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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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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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strategy = StrategyResolver(_CONF).strategy
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@ -131,61 +146,84 @@ def plot_analyzed_dataframe(args: Namespace) -> None:
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)
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exit()
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# Set the ticker to use
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tick_interval = strategy.ticker_interval
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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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# 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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exchange.refresh_tickers([pair], tick_interval)
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tickers[pair] = exchange.klines(pair)
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logger.info('Downloading pairs.')
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exchange.refresh_tickers(pairs, tick_interval)
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for pair in pairs:
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tickers[pair] = exchange.klines(pair)
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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=[pair],
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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, or impossible to download
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if tickers == {}:
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exit()
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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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# Get trades already made from the DB
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trades = load_trades(args, pair, timerange)
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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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if len(dataframe.index) > args.plot_limit:
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logger.warning('Ticker contained more than %s candles as defined '
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'with --plot-limit, clipping.', args.plot_limit)
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dataframe = dataframe.tail(args.plot_limit)
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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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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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args=args
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)
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plot(fig, filename=str(Path('user_data').joinpath('freqtrade-plot.html')))
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return trades
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def generate_graph(pair, trades: pd.DataFrame, data: pd.DataFrame, args) -> tools.make_subplots:
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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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:param args: sys.argv that contrains the two params indicators1, and indicators2
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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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@ -201,6 +239,7 @@ def generate_graph(pair, trades: pd.DataFrame, data: pd.DataFrame, args) -> tool
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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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@ -285,7 +324,7 @@ def generate_graph(pair, trades: pd.DataFrame, data: pd.DataFrame, args) -> tool
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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 = 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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@ -300,7 +339,7 @@ def generate_graph(pair, trades: pd.DataFrame, data: pd.DataFrame, args) -> tool
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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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fig = generate_row(fig=fig, row=3, raw_indicators=indicators2, data=data)
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return fig
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@ -349,7 +388,7 @@ def plot_parse_args(args: List[str]) -> Namespace:
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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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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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@ -366,15 +405,58 @@ def plot_parse_args(args: List[str]) -> Namespace:
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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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plot_analyzed_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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@ -107,8 +107,8 @@ def plot_profit(args: Namespace) -> None:
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exit(1)
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# Take pairs from the cli otherwise switch to the pair in the config file
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if args.pair:
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filter_pairs = args.pair
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if args.pairs:
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filter_pairs = args.pairs
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filter_pairs = filter_pairs.split(',')
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else:
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filter_pairs = config['exchange']['pair_whitelist']
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