import logging from pathlib import Path from typing import Any, Dict, List import pandas as pd from freqtrade.configuration import TimeRange from freqtrade.data import history from freqtrade.data.btanalysis import (combine_tickers_with_mean, create_cum_profit, extract_trades_of_period, load_trades) from freqtrade.resolvers import StrategyResolver logger = logging.getLogger(__name__) try: from plotly.subplots import make_subplots from plotly.offline import plot import plotly.graph_objects as go except ImportError: logger.exception("Module plotly not found \n Please install using `pip3 install plotly`") exit(1) def init_plotscript(config): """ Initialize objects needed for plotting :return: Dict with tickers, trades and pairs """ if "pairs" in config: pairs = config["pairs"] else: pairs = config["exchange"]["pair_whitelist"] # Set timerange to use timerange = TimeRange.parse_timerange(config.get("timerange")) tickers = history.load_data( datadir=Path(str(config.get("datadir"))), pairs=pairs, timeframe=config.get('ticker_interval', '5m'), timerange=timerange, ) trades = load_trades(config['trade_source'], db_url=config.get('db_url'), exportfilename=config.get('exportfilename'), ) trades = history.trim_dataframe(trades, timerange, 'open_time') return {"tickers": tickers, "trades": trades, "pairs": pairs, } def add_indicators(fig, row, indicators: List[str], data: pd.DataFrame) -> make_subplots: """ Generator all the indicator selected by the user for a specific row :param fig: Plot figure to append to :param row: row number for this plot :param indicators: List of indicators present in the dataframe :param data: candlestick DataFrame """ for indicator in indicators: if indicator in data: scatter = go.Scatter( x=data['date'], y=data[indicator].values, mode='lines', name=indicator ) 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 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: trade_buys = go.Scatter( x=trades["open_time"], y=trades["open_rate"], mode='markers', name='trade_buy', marker=dict( symbol='square-open', size=11, line=dict(width=2), color='green' ) ) # Create description for sell summarizing the trade desc = trades.apply(lambda row: f"{round(row['profitperc'], 3)}%, {row['sell_reason']}, " f"{row['duration']} min", axis=1) trade_sells = go.Scatter( x=trades["close_time"], y=trades["close_rate"], text=desc, mode='markers', name='trade_sell', 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) else: logger.warning("No trades found.") return fig def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None, indicators1: List[str] = [], indicators2: List[str] = [],) -> 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 :return: None """ # Define the graph fig = make_subplots( rows=3, cols=1, shared_xaxes=True, row_width=[1, 1, 4], vertical_spacing=0.0001, ) fig['layout'].update(title=pair) fig['layout']['yaxis1'].update(title='Price') fig['layout']['yaxis2'].update(title='Volume') fig['layout']['yaxis3'].update(title='Other') 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.") # TODO: Figure out why scattergl causes problems plotly/plotly.js#2284 if 'bb_lowerband' in data and 'bb_upperband' in data: bb_lower = go.Scatter( x=data.date, y=data.bb_lowerband, showlegend=False, line={'color': 'rgba(255,255,255,0)'}, ) bb_upper = go.Scatter( x=data.date, y=data.bb_upperband, name='Bollinger Band', fill="tonexty", fillcolor="rgba(0,176,246,0.2)", line={'color': 'rgba(255,255,255,0)'}, ) fig.add_trace(bb_lower, 1, 1) fig.add_trace(bb_upper, 1, 1) if 'bb_upperband' in indicators1 and 'bb_lowerband' in indicators1: indicators1.remove('bb_upperband') indicators1.remove('bb_lowerband') # Add indicators to main plot fig = add_indicators(fig=fig, row=1, indicators=indicators1, data=data) fig = plot_trades(fig, trades) # 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 indicators to separate row fig = add_indicators(fig=fig, row=3, indicators=indicators2, data=data) return fig def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame], trades: pd.DataFrame, timeframe: str) -> go.Figure: # Combine close-values for all pairs, rename columns to "pair" df_comb = combine_tickers_with_mean(tickers, "close") # 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') for pair in pairs: profit_col = f'cum_profit_{pair}' 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}") return fig def generate_plot_filename(pair, timeframe) -> str: """ Generate filenames per pair/timeframe to be used for storing plots """ pair_name = pair.replace("/", "_") file_name = 'freqtrade-plot-' + pair_name + '-' + 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 tickers 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(config).strategy plot_elements = init_plotscript(config) trades = plot_elements['trades'] pair_counter = 0 for pair, data in plot_elements["tickers"].items(): pair_counter += 1 logger.info("analyse pair %s", pair) tickers = {} tickers[pair] = data dataframe = strategy.analyze_ticker(tickers[pair], {'pair': pair}) trades_pair = trades.loc[trades['pair'] == pair] trades_pair = extract_trades_of_period(dataframe, trades_pair) fig = generate_candlestick_graph( pair=pair, data=dataframe, trades=trades_pair, indicators1=config["indicators1"], indicators2=config["indicators2"], ) store_plot_file(fig, filename=generate_plot_filename(pair, config['ticker_interval']), 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. """ plot_elements = init_plotscript(config) 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_time'].isnull()) ] # 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["tickers"], trades, config.get('ticker_interval', '5m')) store_plot_file(fig, filename='freqtrade-profit-plot.html', directory=config['user_data_dir'] / "plot", auto_open=True)