From 91378289962fe3b354840a6b3b65aec8ac8817af Mon Sep 17 00:00:00 2001 From: louiscklaw Date: Fri, 31 Jan 2025 19:53:54 +0800 Subject: [PATCH] update, --- hkdse_maths_tutor/meta.md | 5 + .../__pycache__/app.cpython-310.pyc | Bin 0 -> 772 bytes .../__pycache__/app.cpython-311.pyc | Bin 0 -> 1331 bytes .../from_customer/backtest_dashboard/app.py | 27 + .../backtest_dashboard/callbacks/__init__.py | 0 .../__pycache__/__init__.cpython-310.pyc | Bin 0 -> 155 bytes .../__pycache__/__init__.cpython-311.pyc | Bin 0 -> 182 bytes .../backtest_callbacks.cpython-310.pyc | Bin 0 -> 4428 bytes .../backtest_callbacks.cpython-311.pyc | Bin 0 -> 7885 bytes .../remove_strategy_callback.cpython-310.pyc | Bin 0 -> 1390 bytes .../strategy_callbacks.cpython-310.pyc | Bin 0 -> 370 bytes .../strategy_callbacks.cpython-311.pyc | Bin 0 -> 17639 bytes .../callbacks/backtest_callbacks.py | 178 + .../callbacks/strategy/__init__.py | 0 .../__pycache__/__init__.cpython-310.pyc | Bin 0 -> 164 bytes 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callbacks +register_backtest_callbacks(app) +register_strategy_callbacks(app) + +if __name__ == '__main__': + app.run_server(debug=True) \ No newline at end of file diff --git a/hkdse_maths_tutor/quotation1/from_customer/backtest_dashboard/callbacks/__init__.py b/hkdse_maths_tutor/quotation1/from_customer/backtest_dashboard/callbacks/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/hkdse_maths_tutor/quotation1/from_customer/backtest_dashboard/callbacks/__pycache__/__init__.cpython-310.pyc b/hkdse_maths_tutor/quotation1/from_customer/backtest_dashboard/callbacks/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d3c5a8d4374240766ad9a717d48b9771608801bf GIT binary patch literal 155 zcmd1j<>g`k0!iMAbP)X*L?8o3AjbiSi&=m~3PUi1CZpd2 zCSzPui?d7e3u2NIle0@wi%a5D5{omE@)L_vVv-YcazMhxG4b)4d6^~g@p=W7w>WHa S^HWN5Qtd$I7Bc|}76t&MQY2^q literal 0 HcmV?d00001 diff --git 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b/hkdse_maths_tutor/quotation1/from_customer/backtest_dashboard/layouts/components.py @@ -0,0 +1,38 @@ +from dash import html, dcc + +def create_metric_card(label, value, bg_color): + return html.Div([ + html.Div(label, style={ + 'fontSize': '13px', + 'color': '#555', + 'fontWeight': '500', + 'marginBottom': '4px', + 'whiteSpace': 'nowrap' + }), + html.Div(value, style={ + 'fontSize': '15px', + 'fontWeight': 'bold', + 'color': '#2c3e50' + }) + ], style={ + 'padding': '12px', + 'backgroundColor': bg_color, + 'borderRadius': '8px', + 'boxShadow': '0 1px 3px rgba(0,0,0,0.1)', + 'textAlign': 'center', + 'minWidth': '150px' + }) + +def date_range_picker(): + return html.Div([ + html.Label("Select Date Range:", style={'marginRight': '10px'}), + dcc.DatePickerRange( + id='date-range-picker', + start_date=None, + end_date=None + ), + html.Button('Apply', id='apply-date-range', n_clicks=0, + style={'marginLeft': '10px', 'backgroundColor': '#1f77b4', 'color': 'white', 'border': 'none', 'padding': '5px 15px', 'borderRadius': '4px'}), + html.Button('Reset', id='reset-date-range', n_clicks=0, + style={'marginLeft': '10px', 'backgroundColor': '#7f7f7f', 'color': 'white', 'border': 'none', 'padding': '5px 15px', 'borderRadius': '4px'}) + ], style={'marginBottom': '20px'}) \ No newline at end of file diff --git a/hkdse_maths_tutor/quotation1/from_customer/backtest_dashboard/layouts/main_layout.py b/hkdse_maths_tutor/quotation1/from_customer/backtest_dashboard/layouts/main_layout.py new file mode 100644 index 00000000..7c434b75 --- /dev/null +++ b/hkdse_maths_tutor/quotation1/from_customer/backtest_dashboard/layouts/main_layout.py @@ -0,0 +1,432 @@ +from dash import html, dcc +from layouts.components import create_metric_card + +def create_layout(full_data): + return html.Div([ + # Keep your existing stores + dcc.Store(id='backtest-results', storage_type='memory'), + dcc.Store(id='forwardtest-results', storage_type='memory'), + dcc.Store(id='current-params', storage_type='memory'), + # Add new store for selected strategies + dcc.Store(id='selected-strategies', data=[], storage_type='memory'), + + + html.Div([ + html.H1("Backtest Strategy Dashboard", + style={'textAlign': 'center', 'marginBottom': '20px'}), + + # Add the mode switch section here + html.Div([ + html.Div([ + html.Label("Dashboard Mode:", style={ + 'fontWeight': '500', + 'color': '#2c3e50', + 'marginRight': '10px', + 'fontSize': '16px' + }), + dcc.RadioItems( + id='dashboard-mode', + options=[ + {'label': 'Single Strategy Mode', 'value': 'single'}, + {'label': 'Aggregative Mode', 'value': 'aggregative'} + ], + value='single', + style={'display': 'inline-block'}, + inputStyle={'marginRight': '5px'}, + labelStyle={'marginRight': '20px', 'cursor': 'pointer'} + ), + # Changed ID for the second clear selection button + html.Button( + 'Clear Selection', + id='strategy-clear-selection', + style={ + 'marginLeft': '10px', + 'padding': '5px 10px', + 'backgroundColor': '#dc3545', + 'color': 'white', + 'border': 'none', + 'borderRadius': '4px', + 'cursor': 'pointer', + 'display': 'none' # Initially hidden + } + ), + html.Div( + id='selected-count', + style={ + 'marginLeft': '15px', + 'display': 'inline-block', + 'color': '#2c3e50' + } + ) + ], style={'display': 'flex', 'alignItems': 'center'}) + ], style={ + 'marginBottom': '20px', + 'padding': '15px', + 'backgroundColor': '#f8f9fa', + 'borderRadius': '8px', + 'border': '1px solid #e9ecef' + }), + + # Parameters input section + html.Div([ + # First row of inputs + # First row of inputs + html.Div([ + html.Div([ + html.Label("Backtest End Date:", style={ + 'fontWeight': '500', + 'color': '#2c3e50', + 'marginBottom': '5px', + 'display': 'block' + }), + dcc.DatePickerSingle( + id='bt-end-date', + min_date_allowed=full_data.index[0], + max_date_allowed=full_data.index[-1], + initial_visible_month=full_data.index[0], + date=full_data.index[len(full_data) // 2], + display_format='YYYY-MM-DD', + style={'width': '150px'} + ) + ], style={'marginRight': '20px', 'display': 'inline-block'}), + + html.Div([ + html.Label("Time Factor:", style={ + 'fontWeight': '500', + 'color': '#2c3e50', + 'marginBottom': '5px', + 'display': 'block' + }), + dcc.Input( + id='time-factor', + type='number', + value=8760, + style={ + 'width': '100px', + 'height': '36px', + 'padding': '8px', + 'borderRadius': '4px', + 'border': '1px solid #dcdfe6', + 'fontSize': '14px' + } + ) + ], style={'marginRight': '20px', 'display': 'inline-block'}), + + html.Div([ + html.Label("Commission (%):", style={ + 'fontWeight': '500', + 'color': '#2c3e50', + 'marginBottom': '5px', + 'display': 'block' + }), + dcc.Input( + id='commission', + type='number', + value=0.06, + step=0.01, + style={ + 'width': '80px', + 'height': '36px', + 'padding': '8px', + 'borderRadius': '4px', + 'border': '1px solid #dcdfe6', + 'fontSize': '14px' + } + ) + ], style={'marginRight': '20px', 'display': 'inline-block'}), + + html.Div([ + html.Label("Heatmap Title:", style={ + 'fontWeight': '500', + 'color': '#2c3e50', + 'marginBottom': '5px', + 'display': 'block' + }), + dcc.Input( + id='heatmap-title', + type='text', + placeholder='Enter heatmap title', + autoComplete='off', + style={ + 'width': '200px', + 'height': '36px', + 'padding': '8px', + 'borderRadius': '4px', + 'border': '1px solid #dcdfe6', + 'fontSize': '14px' + } + ) + ], style={'display': 'inline-block'}), + ], style={'marginBottom': '15px'}), + + # Second row of inputs + html.Div([ + html.Div([ + html.Label("Window Range:", style={ + 'fontWeight': '500', + 'color': '#2c3e50', + 'marginBottom': '5px', + 'display': 'block' + }), + html.Div([ + dcc.Input( + id='window-start', + type='number', + value=100, + placeholder='Start', + style={ + 'width': '80px', + 'height': '36px', + 'padding': '8px', + 'borderRadius': '4px', + 'border': '1px solid #dcdfe6', + 'fontSize': '14px', + 'marginRight': '10px' + } + ), + dcc.Input( + id='window-end', + type='number', + value=1000, + placeholder='End', + style={ + 'width': '80px', + 'height': '36px', + 'padding': '8px', + 'borderRadius': '4px', + 'border': '1px solid #dcdfe6', + 'fontSize': '14px', + 'marginRight': '10px' + } + ), + dcc.Input( + id='window-step', + type='number', + value=100, + placeholder='Step', + style={ + 'width': '80px', + 'height': '36px', + 'padding': '8px', + 'borderRadius': '4px', + 'border': '1px solid #dcdfe6', + 'fontSize': '14px' + } + ), + ], style={'display': 'flex'}) + ], style={'marginRight': '40px', 'display': 'inline-block'}), + + html.Div([ + html.Label("Threshold Range:", style={ + 'fontWeight': '500', + 'color': '#2c3e50', + 'marginBottom': '5px', + 'display': 'block' + }), + html.Div([ + dcc.Input( + id='threshold-start', + type='number', + value=0.0, + placeholder='Start', + style={ + 'width': '80px', + 'height': '36px', + 'padding': '8px', + 'borderRadius': '4px', + 'border': '1px solid #dcdfe6', + 'fontSize': '14px', + 'marginRight': '10px' + } + ), + dcc.Input( + id='threshold-end', + type='number', + value=2.0, + placeholder='End', + style={ + 'width': '80px', + 'height': '36px', + 'padding': '8px', + 'borderRadius': '4px', + 'border': '1px solid #dcdfe6', + 'fontSize': '14px', + 'marginRight': '10px' + } + ), + dcc.Input( + id='threshold-step', + type='number', + value=0.2, + placeholder='Step', + style={ + 'width': '80px', + 'height': '36px', + 'padding': '8px', + 'borderRadius': '4px', + 'border': '1px solid #dcdfe6', + 'fontSize': '14px' + } + ), + ], style={'display': 'flex'}) + ], style={'display': 'inline-block'}), + ], style={'marginBottom': '15px'}), + + html.Button( + 'Run Backtest', + id='run-backtest', + n_clicks=0, + style={ + 'width': '150px', + 'height': '36px', + 'marginTop': '10px', + 'backgroundColor': '#1a73e8', + 'color': 'white', + 'border': 'none', + 'borderRadius': '4px', + 'cursor': 'pointer', + 'fontSize': '14px', + 'fontWeight': '500' + } + ), + ], style={ + 'padding': '20px', + 'backgroundColor': 'white', + 'borderRadius': '8px', + 'marginBottom': '20px', + 'boxShadow': '0 1px 3px rgba(0,0,0,0.1)' + }), + + # Heatmap Container + html.Div([ + dcc.Graph(id='combined-heatmap'), + ], style={ + 'marginBottom': '30px', + 'backgroundColor': 'white', + 'borderRadius': '10px', + 'padding': '15px', + 'boxShadow': '0 2px 4px rgba(0,0,0,0.1)' + }), + + # Strategy details section + html.Div([ + html.H3("Strategy Details", + id='strategy-header', + style={'textAlign': 'center', 'marginTop': '30px', 'marginBottom': '20px'}), + + # Date range picker section + html.Div([ + html.Label("Select Date Range:", style={'fontWeight': 'bold', 'marginRight': '10px'}), + dcc.DatePickerRange( + id='date-range-picker', + min_date_allowed=full_data.index[0], + max_date_allowed=full_data.index[-1], + start_date=full_data.index[0], + end_date=full_data.index[-1], + display_format='YYYY-MM-DD' + ), + html.Button( + 'Apply', + id='apply-date-range', + style={ + 'marginLeft': '10px', + 'backgroundColor': '#2c3e50', + 'color': 'white', + 'border': 'none', + 'padding': '5px 15px', + 'borderRadius': '5px', + 'cursor': 'pointer' + } + ), + html.Button( + 'Reset', + id='reset-date-range', + style={ + 'marginLeft': '10px', + 'backgroundColor': '#7f7f7f', + 'color': 'white', + 'border': 'none', + 'padding': '5px 15px', + 'borderRadius': '5px', + 'cursor': 'pointer' + } + ) + ], style={ + 'marginBottom': '20px', + 'display': 'flex', + 'alignItems': 'center', + 'justifyContent': 'center' + }), + + # New flex container for metrics and plots + html.Div([ + # Left side - Metrics and Yearly Returns + html.Div([ + # Metrics + html.Div(id='strategy-metrics', style={ + 'display': 'grid', + 'gridTemplateColumns': 'repeat(2, 1fr)', + 'gap': '10px', # Reduced from 20px + 'padding': '10px', # Reduced from 20px + 'backgroundColor': '#f8f9fa', + 'borderRadius': '8px', # Reduced from 10px + 'boxShadow': '0 2px 4px rgba(0,0,0,0.1)', + 'height': 'fit-content', + 'marginBottom': '15px', # Reduced from 20px + 'fontSize': '12px' # Added to reduce text size + }), + + # Yearly Returns + html.Div([ + dcc.Graph(id='yearly-returns-plot') + ], style={ + 'backgroundColor': 'white', + 'borderRadius': '8px', # Reduced from 10px + 'padding': '10px', # Reduced from 15px + 'boxShadow': '0 2px 4px rgba(0,0,0,0.1)', + 'marginTop': '15px' # Reduced from 20px + }) + ], style={ + 'width': '30%', + 'marginRight': '15px' # Reduced from 20px + }), + + # Right side - Plots + html.Div([ + # Equity curve + html.Div([ + dcc.Graph(id='equity-curve') + ], style={ + 'marginBottom': '20px', + 'backgroundColor': 'white', + 'borderRadius': '10px', + 'padding': '15px', + 'boxShadow': '0 2px 4px rgba(0,0,0,0.1)' + }), + + # Underwater plot + html.Div([ + dcc.Graph(id='underwater-plot') + ], style={ + 'backgroundColor': 'white', + 'borderRadius': '10px', + 'padding': '15px', + 'boxShadow': '0 2px 4px rgba(0,0,0,0.1)' + }) + ], style={ + 'width': '70%', # Adjust this value to change the width of the plots section + 'display': 'flex', + 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+1,116 @@ +import pandas as pd +import numpy as np +from utils.helpers import zscore + +def backtest(df, window, threshold, time_factor, commission, mode): + df = df.copy() + df['O2C_chg'] = df['Close'] / df['Open'] - 1 + factor = df.active_addresses + df['indicator'] = zscore(factor, window) + + longCondition = df['indicator'].shift() > threshold + shortCondition = df['indicator'].shift() < -threshold + + df['pos'] = np.where(longCondition, 1, np.where(shortCondition, -1, 0)) + df['pnl'] = df['pos'] * df['O2C_chg'] - commission * abs(df['pos'].diff()) + sharpe = df['pnl'].mean() / df['pnl'].std() * np.sqrt(time_factor) + + if mode == 'heatmap': + return pd.Series([window, threshold, sharpe], index=['window', 'threshold', 'sharpe']) + + elif mode == 'full_report': + ar = df.pnl.mean() * time_factor * 100 + mdd = (df.pnl.cumsum() - df.pnl.cumsum().cummax()).min() * 100 + calmar = ar / abs(mdd) + + beta, _ = np.polyfit(df.Close.pct_change()[1:], df.pnl.dropna(), 1) + num_trades = int( + pd.concat([abs(df.pos.diff()), pd.Series(abs(df.pos).iloc[-1])]).fillna(df.pos.iloc[0]).cumsum().iloc[ + -1] / 2) + long_duration = (len(df.pos[df.pos == 1]) / len(df.pos)) * 100 + short_duration = (len(df.pos[df.pos == -1]) / len(df.pos)) * 100 + + win_rate = len(df.pnl[df.pnl > 0]) / len(df.pnl[df.pnl > 0] + df.pnl[df.pnl < 0]) * 100 + avg_win = df.pnl[df.pnl > 0].mean() * 100 + avg_loss = df.pnl[df.pnl < 0].mean() * 100 + + turnover_pct = (long_duration + short_duration) / num_trades + turnover = turnover_pct * len(df) * 0.01 + + result_dict = { + 'Return (Ann.) [%]': ar, + 'Sharpe Ratio': sharpe, + 'Calmar Ratio': calmar, + 'Max. Drawdown [%]': mdd, + 'Beta': beta, + 'Win Rate [%]': win_rate, + 'Avg Win [%]': avg_win, + 'Avg Loss [%]': avg_loss, + 'Number of Trades': num_trades, + 'Long Duration [%]': long_duration, + 'Short Duration [%]': short_duration, + 'Turnover [%]': turnover_pct, + 'Turnover Period': turnover, + 'Equity Curve': df.pnl.cumsum().tolist(), + 'Benchmark': df.O2C_chg.cumsum().tolist(), + 'Dates': df.index.tolist(), + 'Returns': df.pnl.tolist(), # Added daily PnL + 'Benchmark Returns': df.O2C_chg.tolist() # Added benchmark returns + } + + return result_dict + +def combine_strategies(df, strategies, time_factor, commission): + """ + Combine multiple strategies and calculate aggregate metrics. + + Parameters: + ----------- + df : pandas.DataFrame + Input data with OHLCV columns + strategies : list of dict + List of strategies with 'window' and 'threshold' parameters + time_factor : int + Time factor for annualization + commission : float + Commission rate + + Returns: + -------- + dict + Dictionary containing combined backtest results + """ + combined_pnl = pd.Series(0.0, index=df.index) + combined_benchmark = pd.Series(0.0, index=df.index) + + # Combine strategies + for strategy in strategies: + result = backtest(df, strategy['window'], strategy['threshold'], + time_factor, commission, 'full_report') + combined_pnl += pd.Series(result['Returns'], index=pd.to_datetime(result['Dates'])) + combined_benchmark += pd.Series(result['Benchmark Returns'], index=pd.to_datetime(result['Dates'])) + + # Average the returns + n_strategies = len(strategies) + combined_pnl = combined_pnl / n_strategies + combined_benchmark = combined_benchmark / n_strategies + + # Calculate metrics + ar = combined_pnl.mean() * time_factor * 100 + mdd = (combined_pnl.cumsum() - combined_pnl.cumsum().cummax()).min() * 100 + calmar = ar / abs(mdd) + sharpe = combined_pnl.mean() / combined_pnl.std() * np.sqrt(time_factor) + beta, _ = np.polyfit(df.Close.pct_change()[1:], combined_pnl.dropna(), 1) + + return { + 'Returns': combined_pnl.values, + 'Benchmark Returns': combined_benchmark.values, + 'Dates': df.index, + 'Equity Curve': combined_pnl.cumsum().values, + 'Benchmark': combined_benchmark.cumsum().values, + 'Return (Ann.) 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