700 lines
26 KiB
Python
700 lines
26 KiB
Python
import logging
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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import pandas as pd
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from freqtrade.configuration import TimeRange
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from freqtrade.data.btanalysis import (analyze_trade_parallelism, extract_trades_of_period,
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load_trades)
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from freqtrade.data.converter import trim_dataframe
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.data.history import get_timerange, load_data
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from freqtrade.data.metrics import (calculate_max_drawdown, calculate_underwater,
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combine_dataframes_with_mean, create_cum_profit)
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from freqtrade.enums import CandleType
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from freqtrade.exceptions import OperationalException
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from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_seconds
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from freqtrade.misc import pair_to_filename
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from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
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from freqtrade.resolvers import ExchangeResolver, StrategyResolver
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from freqtrade.strategy import IStrategy
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logger = logging.getLogger(__name__)
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try:
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import plotly.graph_objects as go
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from plotly.offline import plot
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from plotly.subplots import make_subplots
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except ImportError:
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logger.exception("Module plotly not found \n Please install using `pip3 install plotly`")
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exit(1)
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def init_plotscript(config, markets: List, startup_candles: int = 0):
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"""
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Initialize objects needed for plotting
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:return: Dict with candle (OHLCV) data, trades and pairs
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"""
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if "pairs" in config:
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pairs = expand_pairlist(config['pairs'], markets)
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else:
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pairs = expand_pairlist(config['exchange']['pair_whitelist'], markets)
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# Set timerange to use
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timerange = TimeRange.parse_timerange(config.get('timerange'))
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data = load_data(
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datadir=config.get('datadir'),
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pairs=pairs,
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timeframe=config['timeframe'],
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timerange=timerange,
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startup_candles=startup_candles,
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data_format=config.get('dataformat_ohlcv', 'json'),
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candle_type=config.get('candle_type_def', CandleType.SPOT)
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)
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if startup_candles and data:
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min_date, max_date = get_timerange(data)
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logger.info(f"Loading data from {min_date} to {max_date}")
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timerange.adjust_start_if_necessary(timeframe_to_seconds(config['timeframe']),
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startup_candles, min_date)
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no_trades = False
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filename = config.get("exportfilename")
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if config.get("no_trades", False):
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no_trades = True
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elif config['trade_source'] == 'file':
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if not filename.is_dir() and not filename.is_file():
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logger.warning("Backtest file is missing skipping trades.")
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no_trades = True
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try:
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trades = load_trades(
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config['trade_source'],
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db_url=config.get('db_url'),
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exportfilename=filename,
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no_trades=no_trades,
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strategy=config.get('strategy'),
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)
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except ValueError as e:
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raise OperationalException(e) from e
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if not trades.empty:
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trades = trim_dataframe(trades, timerange, 'open_date')
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return {"ohlcv": data,
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"trades": trades,
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"pairs": pairs,
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"timerange": timerange,
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}
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def add_indicators(fig, row, indicators: Dict[str, Dict], data: pd.DataFrame) -> make_subplots:
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"""
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Generate all the indicators selected by the user for a specific row, based on the configuration
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:param fig: Plot figure to append to
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:param row: row number for this plot
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:param indicators: Dict of Indicators with configuration options.
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Dict key must correspond to dataframe column.
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:param data: candlestick DataFrame
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"""
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plot_kinds = {
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'scatter': go.Scatter,
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'bar': go.Bar,
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}
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for indicator, conf in indicators.items():
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logger.debug(f"indicator {indicator} with config {conf}")
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if indicator in data:
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kwargs = {'x': data['date'],
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'y': data[indicator].values,
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'name': indicator
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}
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plot_type = conf.get('type', 'scatter')
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color = conf.get('color')
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if plot_type == 'bar':
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kwargs.update({'marker_color': color or 'DarkSlateGrey',
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'marker_line_color': color or 'DarkSlateGrey'})
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else:
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if color:
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kwargs.update({'line': {'color': color}})
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kwargs['mode'] = 'lines'
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if plot_type != 'scatter':
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logger.warning(f'Indicator {indicator} has unknown plot trace kind {plot_type}'
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f', assuming "scatter".')
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kwargs.update(conf.get('plotly', {}))
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trace = plot_kinds[plot_type](**kwargs)
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fig.add_trace(trace, 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 add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_subplots:
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"""
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Add profit-plot
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:param fig: Plot figure to append to
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:param row: row number for this plot
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:param data: candlestick DataFrame
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:param column: Column to use for plot
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:param name: Name to use
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:return: fig with added profit plot
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"""
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profit = go.Scatter(
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x=data.index,
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y=data[column],
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name=name,
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)
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fig.add_trace(profit, row, 1)
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return fig
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def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
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timeframe: str, starting_balance: float) -> make_subplots:
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"""
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Add scatter points indicating max drawdown
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"""
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try:
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_, highdate, lowdate, _, _, max_drawdown = calculate_max_drawdown(
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trades,
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starting_balance=starting_balance
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)
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drawdown = go.Scatter(
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x=[highdate, lowdate],
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y=[
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df_comb.loc[timeframe_to_prev_date(timeframe, highdate), 'cum_profit'],
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df_comb.loc[timeframe_to_prev_date(timeframe, lowdate), 'cum_profit'],
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],
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mode='markers',
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name=f"Max drawdown {max_drawdown:.2%}",
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text=f"Max drawdown {max_drawdown:.2%}",
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marker=dict(
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symbol='square-open',
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size=9,
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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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fig.add_trace(drawdown, row, 1)
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except ValueError:
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logger.warning("No trades found - not plotting max drawdown.")
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return fig
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def add_underwater(fig, row, trades: pd.DataFrame, starting_balance: float) -> make_subplots:
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"""
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Add underwater plots
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"""
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try:
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underwater = calculate_underwater(
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trades,
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value_col="profit_abs",
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starting_balance=starting_balance
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)
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underwater_plot = go.Scatter(
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x=underwater['date'],
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y=underwater['drawdown'],
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name="Underwater Plot",
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fill='tozeroy',
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fillcolor='#cc362b',
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line={'color': '#cc362b'}
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)
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underwater_plot_relative = go.Scatter(
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x=underwater['date'],
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y=(-underwater['drawdown_relative']),
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name="Underwater Plot (%)",
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fill='tozeroy',
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fillcolor='green',
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line={'color': 'green'}
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)
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fig.add_trace(underwater_plot, row, 1)
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fig.add_trace(underwater_plot_relative, row + 1, 1)
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except ValueError:
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logger.warning("No trades found - not plotting underwater plot")
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return fig
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def add_parallelism(fig, row, trades: pd.DataFrame, timeframe: str) -> make_subplots:
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"""
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Add Chart showing trade parallelism
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"""
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try:
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result = analyze_trade_parallelism(trades, timeframe)
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drawdown = go.Scatter(
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x=result.index,
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y=result['open_trades'],
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name="Parallel trades",
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fill='tozeroy',
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fillcolor='#242222',
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line={'color': '#242222'},
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)
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fig.add_trace(drawdown, row, 1)
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except ValueError:
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logger.warning("No trades found - not plotting Parallelism.")
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return fig
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def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
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"""
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Add trades to "fig"
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"""
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# Trades can be empty
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if trades is not None and len(trades) > 0:
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# Create description for sell summarizing the trade
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trades['desc'] = trades.apply(
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lambda row: f"{row['profit_ratio']:.2%}, " +
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(f"{row['enter_tag']}, " if row['enter_tag'] is not None else "") +
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f"{row['exit_reason']}, " +
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f"{row['trade_duration']} min",
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axis=1)
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trade_buys = go.Scatter(
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x=trades["open_date"],
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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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text=trades["desc"],
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marker=dict(
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symbol='circle-open',
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size=11,
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line=dict(width=2),
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color='cyan'
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)
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)
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trade_sells = go.Scatter(
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x=trades.loc[trades['profit_ratio'] > 0, "close_date"],
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y=trades.loc[trades['profit_ratio'] > 0, "close_rate"],
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text=trades.loc[trades['profit_ratio'] > 0, "desc"],
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mode='markers',
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name='Sell - Profit',
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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_loss = go.Scatter(
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x=trades.loc[trades['profit_ratio'] <= 0, "close_date"],
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y=trades.loc[trades['profit_ratio'] <= 0, "close_rate"],
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text=trades.loc[trades['profit_ratio'] <= 0, "desc"],
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mode='markers',
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name='Sell - Loss',
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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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fig.add_trace(trade_buys, 1, 1)
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fig.add_trace(trade_sells, 1, 1)
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fig.add_trace(trade_sells_loss, 1, 1)
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else:
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logger.warning("No trades found.")
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return fig
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def create_plotconfig(indicators1: List[str], indicators2: List[str],
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plot_config: Dict[str, Dict]) -> Dict[str, Dict]:
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"""
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Combines indicators 1 and indicators 2 into plot_config if necessary
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:param indicators1: List containing Main plot indicators
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:param indicators2: List containing Sub plot indicators
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:param plot_config: Dict of Dicts containing advanced plot configuration
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:return: plot_config - eventually with indicators 1 and 2
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"""
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if plot_config:
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if indicators1:
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plot_config['main_plot'] = {ind: {} for ind in indicators1}
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if indicators2:
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plot_config['subplots'] = {'Other': {ind: {} for ind in indicators2}}
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if not plot_config:
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# If no indicators and no plot-config given, use defaults.
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if not indicators1:
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indicators1 = ['sma', 'ema3', 'ema5']
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if not indicators2:
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indicators2 = ['macd', 'macdsignal']
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# Create subplot configuration if plot_config is not available.
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plot_config = {
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'main_plot': {ind: {} for ind in indicators1},
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'subplots': {'Other': {ind: {} for ind in indicators2}},
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}
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if 'main_plot' not in plot_config:
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plot_config['main_plot'] = {}
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if 'subplots' not in plot_config:
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plot_config['subplots'] = {}
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return plot_config
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def plot_area(fig, row: int, data: pd.DataFrame, indicator_a: str,
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indicator_b: str, label: str = "",
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fill_color: str = "rgba(0,176,246,0.2)") -> make_subplots:
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""" Creates a plot for the area between two traces and adds it to fig.
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:param fig: Plot figure to append to
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:param row: row number for this plot
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:param data: candlestick DataFrame
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:param indicator_a: indicator name as populated in strategy
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:param indicator_b: indicator name as populated in strategy
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:param label: label for the filled area
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:param fill_color: color to be used for the filled area
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:return: fig with added filled_traces plot
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"""
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if indicator_a in data and indicator_b in data:
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# make lines invisible to get the area plotted, only.
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line = {'color': 'rgba(255,255,255,0)'}
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# TODO: Figure out why scattergl causes problems plotly/plotly.js#2284
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trace_a = go.Scatter(x=data.date, y=data[indicator_a],
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showlegend=False,
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line=line)
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trace_b = go.Scatter(x=data.date, y=data[indicator_b], name=label,
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fill="tonexty", fillcolor=fill_color,
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line=line)
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fig.add_trace(trace_a, row, 1)
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fig.add_trace(trace_b, row, 1)
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return fig
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def add_areas(fig, row: int, data: pd.DataFrame, indicators) -> make_subplots:
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""" Adds all area plots (specified in plot_config) to fig.
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:param fig: Plot figure to append to
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:param row: row number for this plot
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:param data: candlestick DataFrame
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:param indicators: dict with indicators. ie.: plot_config['main_plot'] or
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plot_config['subplots'][subplot_label]
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:return: fig with added filled_traces plot
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"""
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for indicator, ind_conf in indicators.items():
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if 'fill_to' in ind_conf:
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indicator_b = ind_conf['fill_to']
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if indicator in data and indicator_b in data:
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label = ind_conf.get('fill_label',
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f'{indicator}<>{indicator_b}')
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fill_color = ind_conf.get('fill_color', 'rgba(0,176,246,0.2)')
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fig = plot_area(fig, row, data, indicator, indicator_b,
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label=label, fill_color=fill_color)
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elif indicator not in data:
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logger.info(
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'Indicator "%s" ignored. Reason: This indicator is not '
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'found in your strategy.', indicator
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)
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elif indicator_b not in data:
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logger.info(
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'fill_to: "%s" ignored. Reason: This indicator is not '
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'in your strategy.', indicator_b
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)
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return fig
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def create_scatter(
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data,
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column_name,
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color,
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direction
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) -> Optional[go.Scatter]:
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if column_name in data.columns:
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df_short = data[data[column_name] == 1]
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if len(df_short) > 0:
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shorts = go.Scatter(
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x=df_short.date,
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y=df_short.close,
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mode='markers',
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name=column_name,
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marker=dict(
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symbol=f"triangle-{direction}-dot",
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size=9,
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line=dict(width=1),
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color=color,
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)
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)
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return shorts
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else:
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logger.warning(f"No {column_name}-signals found.")
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return None
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def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None, *,
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indicators1: List[str] = [],
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indicators2: List[str] = [],
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plot_config: Dict[str, Dict] = {},
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) -> go.Figure:
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"""
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Generate the graph from the data generated by Backtesting or from DB
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Volume will always be ploted in row2, so Row 1 and 3 are to our disposal for custom indicators
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:param pair: Pair to Display on the graph
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:param data: OHLCV DataFrame containing indicators and buy/sell signals
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:param trades: All trades created
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:param indicators1: List containing Main plot indicators
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:param indicators2: List containing Sub plot indicators
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:param plot_config: Dict of Dicts containing advanced plot configuration
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:return: Plotly figure
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"""
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plot_config = create_plotconfig(indicators1, indicators2, plot_config)
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rows = 2 + len(plot_config['subplots'])
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row_widths = [1 for _ in plot_config['subplots']]
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# Define the graph
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fig = make_subplots(
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rows=rows,
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cols=1,
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shared_xaxes=True,
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row_width=row_widths + [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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for i, name in enumerate(plot_config['subplots']):
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fig['layout'][f'yaxis{3 + i}'].update(title=name)
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fig['layout']['xaxis']['rangeslider'].update(visible=False)
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fig.update_layout(modebar_add=["v1hovermode", "toggleSpikeLines"])
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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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fig.add_trace(candles, 1, 1)
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longs = create_scatter(data, 'enter_long', 'green', 'up')
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exit_longs = create_scatter(data, 'exit_long', 'red', 'down')
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shorts = create_scatter(data, 'enter_short', 'blue', 'down')
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exit_shorts = create_scatter(data, 'exit_short', 'violet', 'up')
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for scatter in [longs, exit_longs, shorts, exit_shorts]:
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if scatter:
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fig.add_trace(scatter, 1, 1)
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# Add Bollinger Bands
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fig = plot_area(fig, 1, data, 'bb_lowerband', 'bb_upperband',
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label="Bollinger Band")
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# prevent bb_lower and bb_upper from plotting
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try:
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del plot_config['main_plot']['bb_lowerband']
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del plot_config['main_plot']['bb_upperband']
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except KeyError:
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pass
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# main plot goes to row 1
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fig = add_indicators(fig=fig, row=1, indicators=plot_config['main_plot'], data=data)
|
|
fig = add_areas(fig, 1, data, plot_config['main_plot'])
|
|
fig = plot_trades(fig, trades)
|
|
# sub plot: Volume goes to row 2
|
|
volume = go.Bar(
|
|
x=data['date'],
|
|
y=data['volume'],
|
|
name='Volume',
|
|
marker_color='DarkSlateGrey',
|
|
marker_line_color='DarkSlateGrey'
|
|
)
|
|
fig.add_trace(volume, 2, 1)
|
|
# add each sub plot to a separate row
|
|
for i, label in enumerate(plot_config['subplots']):
|
|
sub_config = plot_config['subplots'][label]
|
|
row = 3 + i
|
|
fig = add_indicators(fig=fig, row=row, indicators=sub_config,
|
|
data=data)
|
|
# fill area between indicators ( 'fill_to': 'other_indicator')
|
|
fig = add_areas(fig, row, data, sub_config)
|
|
|
|
return fig
|
|
|
|
|
|
def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
|
|
trades: pd.DataFrame, timeframe: str, stake_currency: str,
|
|
starting_balance: float) -> go.Figure:
|
|
# Combine close-values for all pairs, rename columns to "pair"
|
|
try:
|
|
df_comb = combine_dataframes_with_mean(data, "close")
|
|
except ValueError:
|
|
raise OperationalException(
|
|
"No data found. Please make sure that data is available for "
|
|
"the timerange and pairs selected.")
|
|
|
|
# Trim trades to available OHLCV data
|
|
trades = extract_trades_of_period(df_comb, trades, date_index=True)
|
|
if len(trades) == 0:
|
|
raise OperationalException('No trades found in selected timerange.')
|
|
|
|
# Add combined cumulative profit
|
|
df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
|
|
|
|
# Plot the pairs average close prices, and total profit growth
|
|
avgclose = go.Scatter(
|
|
x=df_comb.index,
|
|
y=df_comb['mean'],
|
|
name='Avg close price',
|
|
)
|
|
|
|
fig = make_subplots(rows=6, cols=1, shared_xaxes=True,
|
|
row_heights=[1, 1, 1, 0.5, 0.75, 0.75],
|
|
vertical_spacing=0.05,
|
|
subplot_titles=[
|
|
"AVG Close Price",
|
|
"Combined Profit",
|
|
"Profit per pair",
|
|
"Parallelism",
|
|
"Underwater",
|
|
"Relative Drawdown",
|
|
])
|
|
fig['layout'].update(title="Freqtrade Profit plot")
|
|
fig['layout']['yaxis1'].update(title='Price')
|
|
fig['layout']['yaxis2'].update(title=f'Profit {stake_currency}')
|
|
fig['layout']['yaxis3'].update(title=f'Profit {stake_currency}')
|
|
fig['layout']['yaxis4'].update(title='Trade count')
|
|
fig['layout']['yaxis5'].update(title='Underwater Plot')
|
|
fig['layout']['yaxis6'].update(title='Underwater Plot Relative (%)', tickformat=',.2%')
|
|
fig['layout']['xaxis']['rangeslider'].update(visible=False)
|
|
fig.update_layout(modebar_add=["v1hovermode", "toggleSpikeLines"])
|
|
|
|
fig.add_trace(avgclose, 1, 1)
|
|
fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
|
|
fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe, starting_balance)
|
|
fig = add_parallelism(fig, 4, trades, timeframe)
|
|
# Two rows consumed
|
|
fig = add_underwater(fig, 5, trades, starting_balance)
|
|
|
|
for pair in pairs:
|
|
profit_col = f'cum_profit_{pair}'
|
|
try:
|
|
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col,
|
|
timeframe)
|
|
fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
|
|
except ValueError:
|
|
pass
|
|
return fig
|
|
|
|
|
|
def generate_plot_filename(pair: str, timeframe: str) -> str:
|
|
"""
|
|
Generate filenames per pair/timeframe to be used for storing plots
|
|
"""
|
|
pair_s = pair_to_filename(pair)
|
|
file_name = 'freqtrade-plot-' + pair_s + '-' + timeframe + '.html'
|
|
|
|
logger.info('Generate plot file for %s', pair)
|
|
|
|
return file_name
|
|
|
|
|
|
def store_plot_file(fig, filename: str, directory: Path, auto_open: bool = False) -> None:
|
|
"""
|
|
Generate a plot html file from pre populated fig plotly object
|
|
:param fig: Plotly Figure to plot
|
|
:param filename: Name to store the file as
|
|
:param directory: Directory to store the file in
|
|
:param auto_open: Automatically open files saved
|
|
:return: None
|
|
"""
|
|
directory.mkdir(parents=True, exist_ok=True)
|
|
|
|
_filename = directory.joinpath(filename)
|
|
plot(fig, filename=str(_filename),
|
|
auto_open=auto_open)
|
|
logger.info(f"Stored plot as {_filename}")
|
|
|
|
|
|
def load_and_plot_trades(config: Dict[str, Any]):
|
|
"""
|
|
From configuration provided
|
|
- Initializes plot-script
|
|
- Get candle (OHLCV) data
|
|
- Generate Dafaframes populated with indicators and signals based on configured strategy
|
|
- Load trades executed during the selected period
|
|
- Generate Plotly plot objects
|
|
- Generate plot files
|
|
:return: None
|
|
"""
|
|
strategy = StrategyResolver.load_strategy(config)
|
|
|
|
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
|
|
IStrategy.dp = DataProvider(config, exchange)
|
|
strategy.bot_start()
|
|
plot_elements = init_plotscript(config, list(exchange.markets), strategy.startup_candle_count)
|
|
timerange = plot_elements['timerange']
|
|
trades = plot_elements['trades']
|
|
pair_counter = 0
|
|
for pair, data in plot_elements["ohlcv"].items():
|
|
pair_counter += 1
|
|
logger.info("analyse pair %s", pair)
|
|
|
|
df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
|
|
df_analyzed = trim_dataframe(df_analyzed, timerange)
|
|
if not trades.empty:
|
|
trades_pair = trades.loc[trades['pair'] == pair]
|
|
trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
|
|
else:
|
|
trades_pair = trades
|
|
|
|
fig = generate_candlestick_graph(
|
|
pair=pair,
|
|
data=df_analyzed,
|
|
trades=trades_pair,
|
|
indicators1=config.get('indicators1', []),
|
|
indicators2=config.get('indicators2', []),
|
|
plot_config=strategy.plot_config if hasattr(strategy, 'plot_config') else {}
|
|
)
|
|
|
|
store_plot_file(fig, filename=generate_plot_filename(pair, config['timeframe']),
|
|
directory=config['user_data_dir'] / 'plot')
|
|
|
|
logger.info('End of plotting process. %s plots generated', pair_counter)
|
|
|
|
|
|
def plot_profit(config: Dict[str, Any]) -> None:
|
|
"""
|
|
Plots the total profit for all pairs.
|
|
Note, the profit calculation isn't realistic.
|
|
But should be somewhat proportional, and therefor useful
|
|
in helping out to find a good algorithm.
|
|
"""
|
|
if 'timeframe' not in config:
|
|
raise OperationalException('Timeframe must be set in either config or via --timeframe.')
|
|
|
|
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
|
|
plot_elements = init_plotscript(config, list(exchange.markets))
|
|
trades = plot_elements['trades']
|
|
# Filter trades to relevant pairs
|
|
# Remove open pairs - we don't know the profit yet so can't calculate profit for these.
|
|
# Also, If only one open pair is left, then the profit-generation would fail.
|
|
trades = trades[(trades['pair'].isin(plot_elements['pairs']))
|
|
& (~trades['close_date'].isnull())
|
|
]
|
|
if len(trades) == 0:
|
|
raise OperationalException("No trades found, cannot generate Profit-plot without "
|
|
"trades from either Backtest result or database.")
|
|
|
|
# Create an average close price of all the pairs that were involved.
|
|
# this could be useful to gauge the overall market trend
|
|
fig = generate_profit_graph(plot_elements['pairs'], plot_elements['ohlcv'],
|
|
trades, config['timeframe'],
|
|
config.get('stake_currency', ''),
|
|
config.get('available_capital', config['dry_run_wallet']))
|
|
store_plot_file(fig, filename='freqtrade-profit-plot.html',
|
|
directory=config['user_data_dir'] / 'plot',
|
|
auto_open=config.get('plot_auto_open', False))
|