mirror of
https://github.com/freqtrade/freqtrade.git
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717 lines
25 KiB
Python
717 lines
25 KiB
Python
import logging
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from datetime import datetime, timezone
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from pathlib import Path
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import pandas as pd
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import Config
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from freqtrade.data.btanalysis import (
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analyze_trade_parallelism,
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extract_trades_of_period,
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load_trades,
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)
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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 (
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calculate_max_drawdown,
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calculate_underwater,
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combine_dataframes_with_mean,
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create_cum_profit,
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)
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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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from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
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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["dataformat_ohlcv"],
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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(
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timeframe_to_seconds(config["timeframe"]), startup_candles, min_date
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)
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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, df_date_col="open_date")
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return {
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"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"], "y": data[indicator].values, "name": indicator}
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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(
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{
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"marker_color": color or "DarkSlateGrey",
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"marker_line_color": color or "DarkSlateGrey",
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}
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)
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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(
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f"Indicator {indicator} has unknown plot trace kind {plot_type}"
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f', assuming "scatter".'
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)
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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 ' "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(
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fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame, timeframe: str, starting_balance: float
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) -> 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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drawdown = calculate_max_drawdown(trades, starting_balance=starting_balance)
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drawdown = go.Scatter(
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x=[drawdown.high_date, drawdown.low_date],
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y=[
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df_comb.loc[timeframe_to_prev_date(timeframe, drawdown.high_date), "cum_profit"],
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df_comb.loc[timeframe_to_prev_date(timeframe, drawdown.low_date), "cum_profit"],
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],
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mode="markers",
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name=f"Max drawdown {drawdown.relative_account_drawdown:.2%}",
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text=f"Max drawdown {drawdown.relative_account_drawdown:.2%}",
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marker=dict(symbol="square-open", size=9, line=dict(width=2), color="green"),
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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, value_col="profit_abs", 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 exit 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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)
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trade_entries = 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 entry",
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text=trades["desc"],
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marker=dict(symbol="circle-open", size=11, line=dict(width=2), color="cyan"),
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)
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trade_exits = 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="Exit - Profit",
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marker=dict(symbol="square-open", size=11, line=dict(width=2), color="green"),
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)
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trade_exits_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="Exit - Loss",
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marker=dict(symbol="square-open", size=11, line=dict(width=2), color="red"),
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)
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fig.add_trace(trade_entries, 1, 1)
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fig.add_trace(trade_exits, 1, 1)
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fig.add_trace(trade_exits_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(
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indicators1: list[str], indicators2: list[str], plot_config: dict[str, dict]
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) -> 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(
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fig,
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row: int,
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data: pd.DataFrame,
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indicator_a: str,
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indicator_b: str,
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label: str = "",
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fill_color: str = "rgba(0,176,246,0.2)",
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) -> 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], showlegend=False, line=line)
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trace_b = go.Scatter(
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x=data.date,
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y=data[indicator_b],
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name=label,
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fill="tonexty",
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fillcolor=fill_color,
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line=line,
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)
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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", 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(
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fig, row, data, indicator, indicator_b, label=label, fill_color=fill_color
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)
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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.",
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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 ' "in your strategy.",
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indicator_b,
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)
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return fig
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def create_scatter(data, column_name, color, direction) -> go.Scatter | None:
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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(
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pair: str,
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data: pd.DataFrame,
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trades: pd.DataFrame | None = None,
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*,
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indicators1: list[str] | None = None,
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indicators2: list[str] | None = None,
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plot_config: dict[str, dict] | None = None,
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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 plotted 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 entry/exit 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(
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indicators1 or [],
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indicators2 or [],
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plot_config or {},
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)
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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, open=data.open, high=data.high, low=data.low, close=data.close, 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", 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"]
|
|
del plot_config["main_plot"]["bb_upperband"]
|
|
except KeyError:
|
|
pass
|
|
# main plot goes to row 1
|
|
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: Config):
|
|
"""
|
|
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)
|
|
IStrategy.dp = DataProvider(config, exchange)
|
|
strategy.ft_bot_start()
|
|
strategy_safe_wrapper(strategy.bot_loop_start)(current_time=datetime.now(timezone.utc))
|
|
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: Config) -> None:
|
|
"""
|
|
Plots the total profit for all pairs.
|
|
Note, the profit calculation isn't realistic.
|
|
But should be somewhat proportional, and therefore 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)
|
|
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),
|
|
)
|