import logging from pathlib import Path from typing import Any, Dict, List import pandas as pd from freqtrade.configuration import TimeRange from freqtrade.data.btanalysis import (calculate_max_drawdown, combine_dataframes_with_mean, create_cum_profit, extract_trades_of_period, load_trades) from freqtrade.data.converter import trim_dataframe from freqtrade.data.dataprovider import DataProvider from freqtrade.data.history import get_timerange, load_data from freqtrade.exceptions import OperationalException from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_seconds from freqtrade.misc import pair_to_filename from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist from freqtrade.resolvers import ExchangeResolver, StrategyResolver from freqtrade.strategy import IStrategy logger = logging.getLogger(__name__) try: import plotly.graph_objects as go from plotly.offline import plot from plotly.subplots import make_subplots except ImportError: logger.exception("Module plotly not found \n Please install using `pip3 install plotly`") exit(1) def init_plotscript(config, markets: List, startup_candles: int = 0): """ Initialize objects needed for plotting :return: Dict with candle (OHLCV) data, trades and pairs """ if "pairs" in config: pairs = expand_pairlist(config['pairs'], markets) else: pairs = expand_pairlist(config['exchange']['pair_whitelist'], markets) # Set timerange to use timerange = TimeRange.parse_timerange(config.get('timerange')) data = load_data( datadir=config.get('datadir'), pairs=pairs, timeframe=config.get('timeframe', '5m'), timerange=timerange, startup_candles=startup_candles, data_format=config.get('dataformat_ohlcv', 'json'), ) if startup_candles and data: min_date, max_date = get_timerange(data) logger.info(f"Loading data from {min_date} to {max_date}") timerange.adjust_start_if_necessary(timeframe_to_seconds(config.get('timeframe', '5m')), startup_candles, min_date) no_trades = False filename = config.get('exportfilename') if config.get('no_trades', False): no_trades = True elif config['trade_source'] == 'file': if not filename.is_dir() and not filename.is_file(): logger.warning("Backtest file is missing skipping trades.") no_trades = True try: trades = load_trades( config['trade_source'], db_url=config.get('db_url'), exportfilename=filename, no_trades=no_trades, strategy=config.get('strategy'), ) except ValueError as e: raise OperationalException(e) from e trades = trim_dataframe(trades, timerange, 'open_date') return {"ohlcv": data, "trades": trades, "pairs": pairs, "timerange": timerange, } def add_indicators(fig, row, indicators: Dict[str, Dict], data: pd.DataFrame) -> make_subplots: """ Generate all the indicators selected by the user for a specific row, based on the configuration :param fig: Plot figure to append to :param row: row number for this plot :param indicators: Dict of Indicators with configuration options. Dict key must correspond to dataframe column. :param data: candlestick DataFrame """ for indicator, conf in indicators.items(): logger.debug(f"indicator {indicator} with config {conf}") if indicator in data: kwargs = {'x': data['date'], 'y': data[indicator].values, 'mode': 'lines', 'name': indicator } if 'color' in conf: kwargs.update({'line': {'color': conf['color']}}) scatter = go.Scatter( **kwargs ) fig.add_trace(scatter, row, 1) else: logger.info( 'Indicator "%s" ignored. Reason: This indicator is not found ' 'in your strategy.', indicator ) return fig def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_subplots: """ Add profit-plot :param fig: Plot figure to append to :param row: row number for this plot :param data: candlestick DataFrame :param column: Column to use for plot :param name: Name to use :return: fig with added profit plot """ profit = go.Scatter( x=data.index, y=data[column], name=name, ) fig.add_trace(profit, row, 1) return fig def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame, timeframe: str) -> make_subplots: """ Add scatter points indicating max drawdown """ try: max_drawdown, highdate, lowdate = calculate_max_drawdown(trades) drawdown = go.Scatter( x=[highdate, lowdate], y=[ df_comb.loc[timeframe_to_prev_date(timeframe, highdate), 'cum_profit'], df_comb.loc[timeframe_to_prev_date(timeframe, lowdate), 'cum_profit'], ], mode='markers', name=f"Max drawdown {max_drawdown * 100:.2f}%", text=f"Max drawdown {max_drawdown * 100:.2f}%", marker=dict( symbol='square-open', size=9, line=dict(width=2), color='green' ) ) fig.add_trace(drawdown, row, 1) except ValueError: logger.warning("No trades found - not plotting max drawdown.") return fig def plot_trades(fig, trades: pd.DataFrame) -> make_subplots: """ Add trades to "fig" """ # Trades can be empty if trades is not None and len(trades) > 0: # Create description for sell summarizing the trade trades['desc'] = trades.apply(lambda row: f"{round(row['profit_ratio'] * 100, 1)}%, " f"{row['sell_reason']}, " f"{row['trade_duration']} min", axis=1) trade_buys = go.Scatter( x=trades["open_date"], y=trades["open_rate"], mode='markers', name='Trade buy', text=trades["desc"], marker=dict( symbol='circle-open', size=11, line=dict(width=2), color='cyan' ) ) trade_sells = go.Scatter( x=trades.loc[trades['profit_ratio'] > 0, "close_date"], y=trades.loc[trades['profit_ratio'] > 0, "close_rate"], text=trades.loc[trades['profit_ratio'] > 0, "desc"], mode='markers', name='Sell - Profit', marker=dict( symbol='square-open', size=11, line=dict(width=2), color='green' ) ) trade_sells_loss = go.Scatter( x=trades.loc[trades['profit_ratio'] <= 0, "close_date"], y=trades.loc[trades['profit_ratio'] <= 0, "close_rate"], text=trades.loc[trades['profit_ratio'] <= 0, "desc"], mode='markers', name='Sell - Loss', marker=dict( symbol='square-open', size=11, line=dict(width=2), color='red' ) ) fig.add_trace(trade_buys, 1, 1) fig.add_trace(trade_sells, 1, 1) fig.add_trace(trade_sells_loss, 1, 1) else: logger.warning("No trades found.") return fig def create_plotconfig(indicators1: List[str], indicators2: List[str], plot_config: Dict[str, Dict]) -> Dict[str, Dict]: """ Combines indicators 1 and indicators 2 into plot_config if necessary :param indicators1: List containing Main plot indicators :param indicators2: List containing Sub plot indicators :param plot_config: Dict of Dicts containing advanced plot configuration :return: plot_config - eventually with indicators 1 and 2 """ if plot_config: if indicators1: plot_config['main_plot'] = {ind: {} for ind in indicators1} if indicators2: plot_config['subplots'] = {'Other': {ind: {} for ind in indicators2}} if not plot_config: # If no indicators and no plot-config given, use defaults. if not indicators1: indicators1 = ['sma', 'ema3', 'ema5'] if not indicators2: indicators2 = ['macd', 'macdsignal'] # Create subplot configuration if plot_config is not available. plot_config = { 'main_plot': {ind: {} for ind in indicators1}, 'subplots': {'Other': {ind: {} for ind in indicators2}}, } if 'main_plot' not in plot_config: plot_config['main_plot'] = {} if 'subplots' not in plot_config: plot_config['subplots'] = {} return plot_config def plot_area(fig, row: int, data: pd.DataFrame, indicator_a: str, indicator_b: str, label: str = "", fill_color: str = "rgba(0,176,246,0.2)") -> make_subplots: """ Creates a plot for the area between two traces and adds it to fig. :param fig: Plot figure to append to :param row: row number for this plot :param data: candlestick DataFrame :param indicator_a: indicator name as populated in stragetie :param indicator_b: indicator name as populated in stragetie :param label: label for the filled area :param fill_color: color to be used for the filled area :return: fig with added filled_traces plot """ if indicator_a in data and indicator_b in data: # make lines invisible to get the area plotted, only. line = {'color': 'rgba(255,255,255,0)'} # TODO: Figure out why scattergl causes problems plotly/plotly.js#2284 trace_a = go.Scatter(x=data.date, y=data[indicator_a], showlegend=False, line=line) trace_b = go.Scatter(x=data.date, y=data[indicator_b], name=label, fill="tonexty", fillcolor=fill_color, line=line) fig.add_trace(trace_a, row, 1) fig.add_trace(trace_b, row, 1) return fig def add_areas(fig, row: int, data: pd.DataFrame, indicators) -> make_subplots: """ Adds all area plots (specified in plot_config) to fig. :param fig: Plot figure to append to :param row: row number for this plot :param data: candlestick DataFrame :param indicators: dict with indicators. ie.: plot_config['main_plot'] or plot_config['subplots'][subplot_label] :return: fig with added filled_traces plot """ for indicator, ind_conf in indicators.items(): if 'fill_to' in ind_conf: indicator_b = ind_conf['fill_to'] if indicator in data and indicator_b in data: label = ind_conf.get('fill_label', f'{indicator}<>{indicator_b}') fill_color = ind_conf.get('fill_color', 'rgba(0,176,246,0.2)') fig = plot_area(fig, row, data, indicator, indicator_b, label=label, fill_color=fill_color) elif indicator not in data: logger.info( 'Indicator "%s" ignored. Reason: This indicator is not ' 'found in your strategy.', indicator ) elif indicator_b not in data: logger.info( 'fill_to: "%s" ignored. Reason: This indicator is not ' 'in your strategy.', indicator_b ) return fig def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None, *, indicators1: List[str] = [], indicators2: List[str] = [], plot_config: Dict[str, Dict] = {}, ) -> go.Figure: """ Generate the graph from the data generated by Backtesting or from DB Volume will always be ploted in row2, so Row 1 and 3 are to our disposal for custom indicators :param pair: Pair to Display on the graph :param data: OHLCV DataFrame containing indicators and buy/sell signals :param trades: All trades created :param indicators1: List containing Main plot indicators :param indicators2: List containing Sub plot indicators :param plot_config: Dict of Dicts containing advanced plot configuration :return: Plotly figure """ plot_config = create_plotconfig(indicators1, indicators2, plot_config) rows = 2 + len(plot_config['subplots']) row_widths = [1 for _ in plot_config['subplots']] # Define the graph fig = make_subplots( rows=rows, cols=1, shared_xaxes=True, row_width=row_widths + [1, 4], vertical_spacing=0.0001, ) fig['layout'].update(title=pair) fig['layout']['yaxis1'].update(title='Price') fig['layout']['yaxis2'].update(title='Volume') for i, name in enumerate(plot_config['subplots']): fig['layout'][f'yaxis{3 + i}'].update(title=name) fig['layout']['xaxis']['rangeslider'].update(visible=False) # Common information candles = go.Candlestick( x=data.date, open=data.open, high=data.high, low=data.low, close=data.close, name='Price' ) fig.add_trace(candles, 1, 1) if 'buy' in data.columns: df_buy = data[data['buy'] == 1] if len(df_buy) > 0: buys = go.Scatter( x=df_buy.date, y=df_buy.close, mode='markers', name='buy', marker=dict( symbol='triangle-up-dot', size=9, line=dict(width=1), color='green', ) ) fig.add_trace(buys, 1, 1) else: logger.warning("No buy-signals found.") if 'sell' in data.columns: df_sell = data[data['sell'] == 1] if len(df_sell) > 0: sells = go.Scatter( x=df_sell.date, y=df_sell.close, mode='markers', name='sell', marker=dict( symbol='triangle-down-dot', size=9, line=dict(width=1), color='red', ) ) fig.add_trace(sells, 1, 1) else: logger.warning("No sell-signals found.") # Add Bollinger Bands fig = plot_area(fig, 1, data, 'bb_lowerband', 'bb_upperband', label="Bollinger Band") # prevent bb_lower and bb_upper from plotting try: 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) -> go.Figure: # Combine close-values for all pairs, rename columns to "pair" df_comb = combine_dataframes_with_mean(data, "close") # 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=3, cols=1, shared_xaxes=True, row_width=[1, 1, 1], vertical_spacing=0.05, subplot_titles=["AVG Close Price", "Combined Profit", "Profit per pair"]) fig['layout'].update(title="Freqtrade Profit plot") fig['layout']['yaxis1'].update(title='Price') fig['layout']['yaxis2'].update(title='Profit') fig['layout']['yaxis3'].update(title='Profit') fig['layout']['xaxis']['rangeslider'].update(visible=False) 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) 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 excecuted 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) 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) trades_pair = trades.loc[trades['pair'] == pair] trades_pair = extract_trades_of_period(df_analyzed, trades_pair) 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. """ 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.get('timeframe', '5m')) store_plot_file(fig, filename='freqtrade-profit-plot.html', directory=config['user_data_dir'] / 'plot', auto_open=True)