update,
This commit is contained in:
louiscklaw
5
hkdse_maths_tutor/meta.md
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5
hkdse_maths_tutor/meta.md
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---
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tags:
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---
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# notes
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import dash
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from dash import dcc, html
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from utils.helpers import load_data
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from layouts.main_layout import create_layout
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from callbacks.backtest_callbacks import register_backtest_callbacks
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from callbacks.strategy_callbacks import register_strategy_callbacks
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# Create the Dash app
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app = dash.Dash(__name__,
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prevent_initial_callbacks=True,
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suppress_callback_exceptions=True)
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# Load data
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try:
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full_data = load_data()
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except Exception as e:
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raise Exception(f"Error: {str(e)}")
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# Set up the layout
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app.layout = create_layout(full_data)
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# Register callbacks
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register_backtest_callbacks(app)
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register_strategy_callbacks(app)
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if __name__ == '__main__':
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app.run_server(debug=True)
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from dash import Input, Output, State
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from datetime import timedelta
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import pandas as pd
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import numpy as np
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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from models.backtest import backtest
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def register_backtest_callbacks(app):
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@app.callback(
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[Output('backtest-results', 'data'),
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Output('combined-heatmap', 'figure'),
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Output('forwardtest-results', 'data')],
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Input('run-backtest', 'n_clicks'),
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[State('bt-end-date', 'date'),
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State('window-start', 'value'),
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State('window-end', 'value'),
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State('window-step', 'value'),
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State('threshold-start', 'value'),
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State('threshold-end', 'value'),
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State('threshold-step', 'value'),
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State('time-factor', 'value'),
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State('commission', 'value'),
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State('heatmap-title', 'value')], # Add new input for title
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prevent_initial_call=True
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)
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def run_backtest_and_create_heatmaps(n_clicks, bt_end_date, window_start, window_end, window_step,
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threshold_start, threshold_end, threshold_step, time_factor,
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commission, heatmap_title):
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if n_clicks == 0:
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return {}, {}, {}
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try:
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# Get data from the global scope
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from app import full_data
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# Parse inputs
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bt_end_date = pd.to_datetime(bt_end_date)
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ft_start_date = bt_end_date + timedelta(days=1)
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commission = commission / 100
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# Create window and threshold lists
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window_list = list(range(window_start, window_end, window_step))
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threshold_steps = int((threshold_end - threshold_start) / threshold_step)
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threshold_list = [round(threshold_start + i * threshold_step, 2)
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for i in range(threshold_steps)]
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# Split data
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bt_df = full_data[:bt_end_date]
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ft_df = full_data[ft_start_date:]
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# Run backtests
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bt_results = [backtest(bt_df, w, t, time_factor, commission, 'heatmap')
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for w in window_list for t in threshold_list]
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ft_results = [backtest(ft_df, w, t, time_factor, commission, 'heatmap')
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for w in window_list for t in threshold_list]
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# Reverse threshold list for display
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threshold_list.reverse()
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# Process results
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bt_results_df = pd.DataFrame(bt_results)
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ft_results_df = pd.DataFrame(ft_results)
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bt_data_table = bt_results_df.pivot(index='threshold', columns='window', values='sharpe')
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ft_data_table = ft_results_df.pivot(index='threshold', columns='window', values='sharpe')
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# Sort tables
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bt_data_table = bt_data_table.sort_index(ascending=False)
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ft_data_table = ft_data_table.sort_index(ascending=False)
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# Use custom title or default if none provided
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bt_title = f"{heatmap_title}_BT_sharpe" if heatmap_title else "Backtest Sharpe Ratio"
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ft_title = f"{heatmap_title}_FT_sharpe" if heatmap_title else "Forward Test Sharpe Ratio"
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# Create heatmap figure
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fig = make_subplots(
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rows=1, cols=2,
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subplot_titles=(bt_title, ft_title),
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horizontal_spacing=0.17,
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column_widths=[0.5, 0.5]
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)
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# Custom colorscale
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custom_colorscale = [
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[0.0, '#FFFFBF'], # Light yellow
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[0.2, '#E0F3F8'], # Pale blue
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[0.4, '#ABD9E9'], # Very light blue
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[0.6, '#74ADD1'], # Light blue
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[0.8, '#4575B4'], # Blue
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[1.0, '#313695'] # Deep blue
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]
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# Add backtest heatmap
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fig.add_trace(
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go.Heatmap(
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z=bt_data_table.values,
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x=bt_data_table.columns,
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y=np.round(bt_data_table.index, 2),
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text=np.round(bt_data_table.values, 2),
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texttemplate='%{text}',
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textfont={"size": 9},
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colorscale=custom_colorscale,
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showscale=True,
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colorbar=dict(
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x=0.45,
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thickness=15,
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title="Sharpe Ratio",
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titleside="right",
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len=0.9
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)
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),
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row=1, col=1
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)
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# Add forward test heatmap
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fig.add_trace(
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go.Heatmap(
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z=ft_data_table.values,
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x=ft_data_table.columns,
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y=np.round(ft_data_table.index, 2),
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text=np.round(ft_data_table.values, 2),
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texttemplate='%{text}',
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textfont={"size": 9},
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colorscale=custom_colorscale,
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showscale=True,
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colorbar=dict(
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x=1.02,
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thickness=15,
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title="Sharpe Ratio",
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titleside="right",
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len=0.9
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)
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),
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row=1, col=2
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)
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# Calculate dimensions
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cell_height = 25
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cell_width = 15
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height = max(cell_height * len(threshold_list), 500)
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width = max(cell_width * len(window_list) * 3.2, 1800)
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# Update layout
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fig.update_layout(
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height=height,
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width=width,
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showlegend=False,
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title_x=0.5,
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margin=dict(l=50, r=120, t=50, b=50),
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paper_bgcolor='white',
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plot_bgcolor='white',
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font=dict(size=12)
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)
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# Update axes
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for i in range(1, 3):
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fig.update_xaxes(
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title_text="Window",
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tickmode='array',
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tickvals=window_list,
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ticktext=window_list,
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tickangle=45,
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row=1, col=i
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)
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fig.update_yaxes(
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title_text="Threshold",
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tickmode='array',
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tickvals=threshold_list,
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ticktext=[f"{x:.2f}" for x in threshold_list],
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row=1, col=i
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)
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return bt_results_df.to_dict('records'), fig, ft_results_df.to_dict('records')
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except Exception as e:
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print(f"Error in heatmap generation: {str(e)}")
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return {}, {}, {}
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import plotly.graph_objects as go
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import pandas as pd
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from dash import html
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from layouts.components import create_metric_card
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def create_equity_figure(result_dict):
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"""
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Creates an equity curve figure comparing strategy performance against benchmark.
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"""
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fig = go.Figure()
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fig.add_trace(go.Scatter(
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x=pd.to_datetime(result_dict['Dates']),
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y=result_dict['Equity Curve'],
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name='Strategy',
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line=dict(color='#2C7FB8', width=2)
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))
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fig.add_trace(go.Scatter(
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x=pd.to_datetime(result_dict['Dates']),
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y=result_dict['Benchmark'],
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name='Benchmark',
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line=dict(color='#FC8D59', width=2)
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))
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fig.update_layout(
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title={
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'text': "Equity Curve",
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'y': 0.95,
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'x': 0.5,
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'xanchor': 'center',
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'yanchor': 'top',
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'font': dict(size=16)
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},
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xaxis=dict(
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type="date",
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showgrid=True
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),
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yaxis_title="Cumulative Return",
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template='plotly_white',
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height=500,
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hovermode='x unified',
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legend=dict(
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yanchor="top",
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y=0.99,
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xanchor="left",
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x=0.01,
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bgcolor='rgba(255, 255, 255, 0.8)'
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),
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margin=dict(l=50, r=50, t=50, b=50)
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)
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return fig
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def create_underwater_figure(result_dict):
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"""
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Creates an underwater (drawdown) plot.
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"""
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underwater = pd.Series(result_dict['Equity Curve']) - pd.Series(result_dict['Equity Curve']).cummax()
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fig = go.Figure()
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fig.add_trace(go.Scatter(
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x=pd.to_datetime(result_dict['Dates']),
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y=underwater,
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fill='tozeroy',
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name='Drawdown',
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line=dict(color='#2C7FB8', width=2),
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fillcolor='rgba(44, 127, 184, 0.3)'
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))
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fig.update_layout(
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title={
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'text': "Drawdown",
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'y': 0.95,
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'x': 0.5,
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'xanchor': 'center',
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'yanchor': 'top',
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'font': dict(size=16)
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},
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xaxis=dict(
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type="date",
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showgrid=True
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),
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yaxis_title="Drawdown",
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template='plotly_white',
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height=300,
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hovermode='x unified',
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showlegend=False,
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margin=dict(l=50, r=50, t=50, b=50)
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)
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return fig
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def create_yearly_returns_figure(result_dict):
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"""
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Creates a yearly returns comparison bar plot.
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"""
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try:
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dates = pd.to_datetime(result_dict['Dates'])
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daily_returns = result_dict.get('Returns', None)
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if daily_returns is None:
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daily_returns = pd.Series(result_dict['Equity Curve'], index=dates).diff()
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daily_returns = pd.DataFrame({
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'equity': daily_returns,
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'benchmark': pd.Series(result_dict['Benchmark'], index=dates).diff()
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})
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yearly_returns = daily_returns.resample('Y').sum()
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=yearly_returns.index.year,
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y=yearly_returns['equity'],
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name='Strategy',
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marker_color='#2C7FB8'
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))
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fig.add_trace(go.Bar(
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x=yearly_returns.index.year,
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y=yearly_returns['benchmark'],
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name='Benchmark',
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marker_color='#FC8D59'
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))
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fig.add_hline(y=0, line_dash="dash", line_color="red", opacity=0.5)
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fig.update_layout(
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title={
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'text': "Yearly Returns",
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'y': 0.95,
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'x': 0.5,
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'xanchor': 'center',
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'yanchor': 'top',
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'font': dict(size=16)
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},
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yaxis_title="Returns",
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barmode='group',
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template='plotly_white',
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showlegend=True,
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legend=dict(
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orientation="h",
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y=1.1,
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x=0.5,
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xanchor="center",
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yanchor="bottom"
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),
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height=300,
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margin=dict(l=50, r=50, t=80, b=50),
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bargap=0.2,
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bargroupgap=0.1
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)
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fig.update_xaxes(
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tickangle=0,
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dtick=1,
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tickfont=dict(size=12),
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range=[yearly_returns.index.year.min() - 0.5, yearly_returns.index.year.max() + 0.5]
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)
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fig.update_yaxes(
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title_font=dict(size=12),
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tickfont=dict(size=12),
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zeroline=True,
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zerolinewidth=1,
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zerolinecolor='red'
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)
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return fig
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except Exception as e:
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print(f"Error in create_yearly_returns_plot: {str(e)}")
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return go.Figure()
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def create_metric_cards(result_dict):
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"""
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Creates metric cards displaying strategy performance metrics.
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"""
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metrics_data = [
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('Return (Ann.)', f"{result_dict['Return (Ann.) [%]']:.2f}%", '#e3f2fd'),
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('Sharpe Ratio', f"{result_dict['Sharpe Ratio']:.2f}", '#e3f2fd'),
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('Calmar Ratio', f"{result_dict['Calmar Ratio']:.2f}", '#e3f2fd'),
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('Max Drawdown', f"{result_dict['Max. Drawdown [%]']:.2f}%", '#e3f2fd'),
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('Beta', f"{result_dict['Beta']:.3f}", '#e3f2fd')
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]
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if 'Win Rate [%]' in result_dict:
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trading_metrics = [
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('Win Rate', f"{result_dict['Win Rate [%]']:.2f}%", '#f1f8e9'),
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('Avg Win', f"{result_dict['Avg Win [%]']:.2f}%", '#f1f8e9'),
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('Avg Loss', f"{result_dict['Avg Loss [%]']:.2f}%", '#f1f8e9'),
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('Number of Trades', f"{result_dict['Number of Trades']}", '#f1f8e9'),
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('Long Duration', f"{result_dict['Long Duration [%]']:.2f}%", '#fff3e0'),
|
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('Short Duration', f"{result_dict['Short Duration [%]']:.2f}%", '#fff3e0'),
|
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('Holding Time [%]', f"{result_dict['Turnover [%]']:.2f}%", '#fff3e0'),
|
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('Holding Time', f"{result_dict['Turnover Period']:.2f}", '#fff3e0')
|
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]
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metrics_data.extend(trading_metrics)
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metrics_style = {
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'padding': '12px',
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'backgroundColor': 'white',
|
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'borderRadius': '8px',
|
||||
'boxShadow': '0 1px 3px rgba(0,0,0,0.1)',
|
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'textAlign': 'center',
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'minWidth': '150px',
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'transition': 'transform 0.2s ease-in-out'
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}
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metric_cards = []
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for label, value, bg_color in metrics_data:
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current_style = metrics_style.copy()
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current_style['backgroundColor'] = bg_color
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metric_cards.append(
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html.Div([
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html.Div(label, style={
|
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'fontSize': '13px',
|
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'color': '#555',
|
||||
'fontWeight': '500',
|
||||
'marginBottom': '4px',
|
||||
'whiteSpace': 'nowrap'
|
||||
}),
|
||||
html.Div(value, style={
|
||||
'fontSize': '15px',
|
||||
'fontWeight': 'bold',
|
||||
'color': '#2c3e50'
|
||||
})
|
||||
], style=current_style)
|
||||
)
|
||||
|
||||
return metric_cards
|
||||
|
||||
def create_strategy_header(mode, current_selection, start_date, end_date, click_data=None):
|
||||
"""
|
||||
Creates the strategy header with parameters and period information.
|
||||
"""
|
||||
if mode == 'aggregative' and current_selection:
|
||||
strategy_buttons = [
|
||||
html.Div([
|
||||
html.Span(
|
||||
f"({s['window']}, {s['threshold']:.2f})",
|
||||
style={'marginRight': '4px'}
|
||||
),
|
||||
html.Button(
|
||||
"×",
|
||||
id={'type': 'remove-strategy', 'index': i},
|
||||
n_clicks=0,
|
||||
style={
|
||||
'border': 'none',
|
||||
'background': 'transparent',
|
||||
'color': '#666',
|
||||
'fontSize': '18px',
|
||||
'fontWeight': 'bold',
|
||||
'cursor': 'pointer',
|
||||
'padding': '2px 8px',
|
||||
'verticalAlign': 'middle',
|
||||
'lineHeight': '1',
|
||||
'transition': 'color 0.2s',
|
||||
'margin': '0 2px',
|
||||
'borderRadius': '50%',
|
||||
}
|
||||
)
|
||||
], style={
|
||||
'display': 'inline-flex',
|
||||
'alignItems': 'center',
|
||||
'margin': '2px 4px',
|
||||
'padding': '4px 8px',
|
||||
'backgroundColor': '#f0f0f0',
|
||||
'border': '1px solid #ddd',
|
||||
'borderRadius': '16px',
|
||||
'fontSize': '14px'
|
||||
}) for i, s in enumerate(current_selection)
|
||||
]
|
||||
|
||||
return html.Div([
|
||||
html.Div("Combined Strategy Details",
|
||||
style={'fontSize': '18px', 'fontWeight': 'bold', 'marginBottom': '8px'}),
|
||||
html.Div([
|
||||
html.Span("Parameters: ", style={'marginRight': '8px'}),
|
||||
html.Div(strategy_buttons, style={'display': 'inline-block'})
|
||||
], style={'marginBottom': '8px'}),
|
||||
html.Div(
|
||||
f"Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}"
|
||||
if start_date and end_date else ""
|
||||
)
|
||||
])
|
||||
|
||||
elif click_data:
|
||||
window = click_data['points'][0]['x']
|
||||
threshold = click_data['points'][0]['y']
|
||||
return html.Div([
|
||||
html.Div("Strategy Details",
|
||||
style={'fontSize': '18px', 'fontWeight': 'bold', 'marginBottom': '8px'}),
|
||||
html.Div([
|
||||
html.Span("Parameters: ", style={'marginRight': '8px'}),
|
||||
html.Span(f"({window}, {threshold:.2f})")
|
||||
], style={'marginBottom': '8px'}),
|
||||
html.Div(
|
||||
f"Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}"
|
||||
if start_date and end_date else ""
|
||||
)
|
||||
])
|
||||
|
||||
return html.Div("Click on heatmap to see strategy details")
|
||||
|
||||
def create_error_message(error_type, start_date=None, end_date=None, error_message=None):
|
||||
"""
|
||||
Creates error message displays.
|
||||
"""
|
||||
if error_type == "no_trade":
|
||||
return html.Div([
|
||||
html.Div("No Trade Data",
|
||||
style={'fontSize': '18px', 'fontWeight': 'bold', 'marginBottom': '8px'}),
|
||||
html.Div(
|
||||
f"There is no trade from {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}"
|
||||
)
|
||||
])
|
||||
else:
|
||||
return html.Div([
|
||||
html.Div("Error",
|
||||
style={'fontSize': '18px', 'fontWeight': 'bold', 'marginBottom': '8px'}),
|
||||
html.Div(f"An error occurred: {error_message}")
|
||||
])
|
||||
|
||||
def create_metrics_component(result_dict):
|
||||
"""Create metrics component with grid layout using create_metric_card."""
|
||||
if result_dict is None or not isinstance(result_dict, dict):
|
||||
return html.Div() # Just return an empty div without styling
|
||||
|
||||
try:
|
||||
# Define metrics in a grid-friendly format
|
||||
metrics_grid = [
|
||||
[
|
||||
('Return (Ann.)', f"{result_dict.get('Return (Ann.) [%]', 0):.2f}%"),
|
||||
('Sharpe Ratio', f"{result_dict.get('Sharpe Ratio', 0):.2f}")
|
||||
],
|
||||
[
|
||||
('Calmar Ratio', f"{result_dict.get('Calmar Ratio', 0):.2f}"),
|
||||
('Max Drawdown', f"{result_dict.get('Max. Drawdown [%]', 0):.2f}%")
|
||||
],
|
||||
[
|
||||
('Beta', f"{result_dict.get('Beta', 0):.3f}"),
|
||||
(None, None)
|
||||
]
|
||||
]
|
||||
|
||||
# Create grid layout
|
||||
grid_rows = []
|
||||
for row in metrics_grid:
|
||||
row_cells = []
|
||||
for label, value in row:
|
||||
if label is not None:
|
||||
cell = create_metric_card(label, value, '#e3f2fd')
|
||||
row_cells.append(cell)
|
||||
|
||||
grid_rows.append(html.Div(row_cells, style={
|
||||
'display': 'flex',
|
||||
'justifyContent': 'flex-start',
|
||||
'gap': '10px',
|
||||
'marginBottom': '10px'
|
||||
}))
|
||||
|
||||
return html.Div(grid_rows) # Remove the container styling
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in create_metrics_component: {str(e)}")
|
||||
return html.Div() # Return empty div without styling
|
||||
@@ -0,0 +1,26 @@
|
||||
from dash import Output
|
||||
|
||||
STRATEGY_OUTPUTS = [
|
||||
Output('strategy-metrics', 'children', allow_duplicate=True),
|
||||
Output('equity-curve', 'figure', allow_duplicate=True),
|
||||
Output('underwater-plot', 'figure', allow_duplicate=True),
|
||||
Output('yearly-returns-plot', 'figure', allow_duplicate=True),
|
||||
Output('strategy-header', 'children', allow_duplicate=True),
|
||||
Output('date-range-picker', 'start_date', allow_duplicate=True),
|
||||
Output('date-range-picker', 'end_date', allow_duplicate=True),
|
||||
Output('selected-strategies', 'data', allow_duplicate=True),
|
||||
Output('strategy-clear-selection', 'style', allow_duplicate=True),
|
||||
Output('selected-count', 'children', allow_duplicate=True)
|
||||
]
|
||||
|
||||
def get_base_style(mode):
|
||||
return {
|
||||
'marginLeft': '10px',
|
||||
'padding': '5px 10px',
|
||||
'backgroundColor': '#dc3545',
|
||||
'color': 'white',
|
||||
'border': 'none',
|
||||
'borderRadius': '4px',
|
||||
'cursor': 'pointer',
|
||||
'display': 'block' if mode == 'aggregative' else 'none'
|
||||
}
|
||||
@@ -0,0 +1,97 @@
|
||||
# callbacks/strategy/helpers.py
|
||||
from dash import html
|
||||
import pandas as pd
|
||||
from models.backtest import backtest, combine_strategies
|
||||
from utils.helpers import get_default_date_range
|
||||
from .constants import get_base_style
|
||||
from .components import (
|
||||
create_equity_figure,
|
||||
create_underwater_figure,
|
||||
create_yearly_returns_figure,
|
||||
create_metric_cards,
|
||||
create_strategy_header,
|
||||
create_error_message
|
||||
)
|
||||
|
||||
def get_default_outputs():
|
||||
"""Return default outputs when no interaction has occurred."""
|
||||
return [], {}, {}, {}, html.Div("Click on heatmap to see strategy details"), None, None, [], {'display': 'none'}, ''
|
||||
|
||||
def handle_mode_change(trigger_id, mode, current_selection):
|
||||
"""Handle mode changes and clear selection."""
|
||||
if trigger_id == 'strategy-clear-selection' or mode == 'single':
|
||||
current_selection = []
|
||||
|
||||
selected_count = f'Selected strategies: {len(current_selection)}' if mode == 'aggregative' else ''
|
||||
from app import full_data
|
||||
default_start, default_end = get_default_date_range(full_data)
|
||||
|
||||
return [], {}, {}, {}, html.Div("Click on heatmap to see strategy details"), \
|
||||
default_start, default_end, current_selection, get_base_style(mode), selected_count
|
||||
|
||||
def get_date_range(trigger_id, start_date, end_date):
|
||||
"""Handle date range reset and processing."""
|
||||
if trigger_id == 'reset-date-range':
|
||||
from app import full_data
|
||||
start_date, end_date = get_default_date_range(full_data)
|
||||
|
||||
if start_date and end_date:
|
||||
start_date = pd.to_datetime(start_date)
|
||||
end_date = pd.to_datetime(end_date)
|
||||
|
||||
return start_date, end_date
|
||||
|
||||
def get_filtered_data(start_date, end_date):
|
||||
"""Get data filtered by date range."""
|
||||
from app import full_data
|
||||
if start_date and end_date:
|
||||
return full_data[start_date:end_date]
|
||||
return full_data
|
||||
|
||||
def update_strategy_selection(mode, trigger_id, click_data, current_selection):
|
||||
"""Update strategy selection based on mode and user interaction."""
|
||||
if mode == 'aggregative' and trigger_id == 'combined-heatmap' and click_data:
|
||||
new_strategy = {
|
||||
'window': click_data['points'][0]['x'],
|
||||
'threshold': click_data['points'][0]['y']
|
||||
}
|
||||
if new_strategy not in current_selection:
|
||||
return current_selection + [new_strategy]
|
||||
elif mode == 'single':
|
||||
return []
|
||||
return current_selection
|
||||
|
||||
def get_backtest_results(mode, current_selection, data, click_data, time_factor, commission):
|
||||
"""Get backtest results based on mode and selected strategies."""
|
||||
if mode == 'single' or not current_selection:
|
||||
window = click_data['points'][0]['x']
|
||||
threshold = click_data['points'][0]['y']
|
||||
return backtest(data, window, threshold, time_factor, commission, 'full_report')
|
||||
return combine_strategies(data, current_selection, time_factor, commission)
|
||||
|
||||
def create_output_components(result_dict, mode, current_selection, start_date, end_date, click_data):
|
||||
"""Create all visual components."""
|
||||
metric_cards = create_metric_cards(result_dict)
|
||||
equity_fig = create_equity_figure(result_dict)
|
||||
underwater_fig = create_underwater_figure(result_dict)
|
||||
yearly_returns_fig = create_yearly_returns_figure(result_dict)
|
||||
header_text = create_strategy_header(mode, current_selection, start_date, end_date, click_data)
|
||||
|
||||
base_style = get_base_style(mode)
|
||||
selected_count = f'Selected strategies: {len(current_selection)}' if mode == 'aggregative' else ''
|
||||
|
||||
return (metric_cards, equity_fig, underwater_fig, yearly_returns_fig,
|
||||
header_text, start_date, end_date, current_selection,
|
||||
base_style, selected_count)
|
||||
|
||||
def handle_error(error_type, start_date, end_date, current_selection, mode, error_message=None):
|
||||
"""Handle various error cases."""
|
||||
base_style = get_base_style(mode)
|
||||
selected_count = f'Selected strategies: {len(current_selection)}' if mode == 'aggregative' else ''
|
||||
|
||||
if error_type == "zero_division":
|
||||
error_msg = create_error_message("no_trade", start_date, end_date)
|
||||
else:
|
||||
error_msg = create_error_message("error", error_message=error_message)
|
||||
|
||||
return [], {}, {}, {}, error_msg, start_date, end_date, current_selection, base_style, selected_count
|
||||
@@ -0,0 +1,187 @@
|
||||
from dash import Input, Output, State, html, ALL, callback_context
|
||||
import dash
|
||||
from .constants import STRATEGY_OUTPUTS
|
||||
from .helpers import (
|
||||
get_filtered_data,
|
||||
get_backtest_results,
|
||||
create_output_components
|
||||
)
|
||||
import pandas as pd
|
||||
from .components import (
|
||||
create_equity_figure,
|
||||
create_underwater_figure,
|
||||
create_yearly_returns_figure,
|
||||
create_metrics_component
|
||||
)
|
||||
|
||||
def register_remove_callback(app):
|
||||
@app.callback(
|
||||
[Output('selected-strategies', 'data'),
|
||||
Output('selected-count', 'children'),
|
||||
Output('equity-curve', 'figure'),
|
||||
Output('underwater-plot', 'figure'),
|
||||
Output('yearly-returns-plot', 'figure'),
|
||||
Output('strategy-header', 'children'),
|
||||
Output('strategy-metrics', 'children')],
|
||||
[Input({'type': 'remove-strategy', 'index': ALL}, 'n_clicks')],
|
||||
[State('selected-strategies', 'data'),
|
||||
State('backtest-results', 'data'),
|
||||
State('time-factor', 'value'),
|
||||
State('commission', 'value'),
|
||||
State('date-range-picker', 'start_date'),
|
||||
State('date-range-picker', 'end_date'),
|
||||
State('dashboard-mode', 'value')],
|
||||
prevent_initial_call=True
|
||||
)
|
||||
def remove_strategy(n_clicks_list, current_selection, backtest_results,
|
||||
time_factor, commission, start_date, end_date, mode):
|
||||
if not callback_context.triggered:
|
||||
raise dash.exceptions.PreventUpdate
|
||||
|
||||
trigger = callback_context.triggered[0]
|
||||
if trigger['value'] is None or trigger['value'] == 0:
|
||||
raise dash.exceptions.PreventUpdate
|
||||
|
||||
button_id = eval(trigger['prop_id'].split('.')[0])
|
||||
index_to_remove = button_id['index']
|
||||
|
||||
# Remove the strategy at the specified index
|
||||
updated_selection = [
|
||||
strategy for i, strategy in enumerate(current_selection)
|
||||
if i != index_to_remove
|
||||
]
|
||||
|
||||
# Convert string dates to datetime objects
|
||||
start_date = pd.to_datetime(start_date).date() if start_date else None
|
||||
end_date = pd.to_datetime(end_date).date() if end_date else None
|
||||
|
||||
try:
|
||||
# Get filtered data
|
||||
data = get_filtered_data(start_date, end_date)
|
||||
|
||||
# If there are still strategies left
|
||||
if updated_selection:
|
||||
# Recalculate backtest results for remaining strategies
|
||||
result_dict = get_backtest_results(
|
||||
mode='combined',
|
||||
current_selection=updated_selection,
|
||||
data=data,
|
||||
click_data=None,
|
||||
time_factor=float(time_factor),
|
||||
commission=float(commission) / 100
|
||||
)
|
||||
|
||||
# Create header with improved styling
|
||||
header = html.Div([
|
||||
html.Div("Combined Strategy Details",
|
||||
style={'fontSize': '18px', 'fontWeight': 'bold', 'marginBottom': '8px'}),
|
||||
html.Div([
|
||||
html.Span("Parameters: ", style={'marginRight': '8px'}),
|
||||
html.Div([
|
||||
html.Div([
|
||||
html.Span(f"({s['window']}, {s['threshold']:.2f})",
|
||||
style={'marginRight': '4px'}),
|
||||
html.Button(
|
||||
"×",
|
||||
id={'type': 'remove-strategy', 'index': i},
|
||||
n_clicks=0,
|
||||
style={
|
||||
'border': 'none',
|
||||
'background': 'transparent',
|
||||
'color': '#666',
|
||||
'fontSize': '18px',
|
||||
'fontWeight': 'bold',
|
||||
'cursor': 'pointer',
|
||||
'padding': '2px 8px',
|
||||
'verticalAlign': 'middle',
|
||||
'lineHeight': '1',
|
||||
'transition': 'color 0.2s',
|
||||
'margin': '0 2px',
|
||||
'borderRadius': '50%'
|
||||
}
|
||||
)
|
||||
], style={
|
||||
'display': 'inline-flex',
|
||||
'alignItems': 'center',
|
||||
'margin': '2px 4px',
|
||||
'padding': '4px 8px',
|
||||
'backgroundColor': '#f0f0f0',
|
||||
'border': '1px solid #ddd',
|
||||
'borderRadius': '16px',
|
||||
'fontSize': '14px'
|
||||
}) for i, s in enumerate(updated_selection)
|
||||
], style={'display': 'inline-block'})
|
||||
], style={'marginBottom': '8px'}),
|
||||
html.Div(f"Period: {start_date} to {end_date}")
|
||||
])
|
||||
|
||||
# Create figures
|
||||
equity_fig = create_equity_figure(result_dict)
|
||||
underwater_fig = create_underwater_figure(result_dict)
|
||||
yearly_returns_fig = create_yearly_returns_figure(result_dict)
|
||||
metrics = create_metrics_component(result_dict)
|
||||
|
||||
else:
|
||||
# If no strategies left, return empty state
|
||||
empty_fig = {
|
||||
'data': [],
|
||||
'layout': {
|
||||
'title': {
|
||||
'text': 'No data to display',
|
||||
'y': 0.95,
|
||||
'x': 0.5,
|
||||
'xanchor': 'center',
|
||||
'yanchor': 'top',
|
||||
'font': dict(size=16)
|
||||
},
|
||||
'showlegend': False,
|
||||
'height': 300,
|
||||
'template': 'plotly_white',
|
||||
'xaxis': {'showgrid': False},
|
||||
'yaxis': {'showgrid': False},
|
||||
'margin': dict(l=50, r=50, t=50, b=50)
|
||||
}
|
||||
}
|
||||
|
||||
header = html.Div([
|
||||
html.Div("Strategy Parameters",
|
||||
style={'fontSize': '18px', 'fontWeight': 'bold', 'marginBottom': '8px'}),
|
||||
html.Div("No strategies selected. Click on the heatmap to select strategies.")
|
||||
])
|
||||
|
||||
equity_fig = underwater_fig = yearly_returns_fig = empty_fig
|
||||
metrics = create_metrics_component(None) # Will return empty white container
|
||||
|
||||
return (
|
||||
updated_selection,
|
||||
f"Selected strategies: {len(updated_selection)}",
|
||||
equity_fig,
|
||||
underwater_fig,
|
||||
yearly_returns_fig,
|
||||
header,
|
||||
metrics
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in remove_strategy callback: {str(e)}")
|
||||
empty_fig = {
|
||||
'data': [],
|
||||
'layout': {
|
||||
'title': 'Error occurred',
|
||||
'showlegend': False
|
||||
}
|
||||
}
|
||||
error_header = html.Div([
|
||||
html.Div("Error",
|
||||
style={'fontSize': '18px', 'fontWeight': 'bold', 'marginBottom': '8px'}),
|
||||
html.Div(f"An error occurred: {str(e)}")
|
||||
])
|
||||
return (
|
||||
updated_selection,
|
||||
f"Selected strategies: {len(updated_selection)}",
|
||||
empty_fig,
|
||||
empty_fig,
|
||||
empty_fig,
|
||||
error_header,
|
||||
create_metrics_component(None) # Will return empty white container
|
||||
)
|
||||
@@ -0,0 +1,64 @@
|
||||
# callbacks/strategy/update_callback.py
|
||||
from dash import Input, Output, State
|
||||
import dash
|
||||
from .constants import STRATEGY_OUTPUTS
|
||||
from .helpers import (
|
||||
get_default_outputs,
|
||||
handle_mode_change,
|
||||
get_date_range,
|
||||
get_filtered_data,
|
||||
update_strategy_selection,
|
||||
get_backtest_results,
|
||||
create_output_components,
|
||||
handle_error
|
||||
)
|
||||
|
||||
def register_update_callback(app):
|
||||
@app.callback(
|
||||
STRATEGY_OUTPUTS,
|
||||
[Input('combined-heatmap', 'clickData'),
|
||||
Input('apply-date-range', 'n_clicks'),
|
||||
Input('reset-date-range', 'n_clicks'),
|
||||
Input('dashboard-mode', 'value'),
|
||||
Input('strategy-clear-selection', 'n_clicks')],
|
||||
[State('bt-end-date', 'date'),
|
||||
State('time-factor', 'value'),
|
||||
State('commission', 'value'),
|
||||
State('date-range-picker', 'start_date'),
|
||||
State('date-range-picker', 'end_date'),
|
||||
State('selected-strategies', 'data')],
|
||||
prevent_initial_call=True
|
||||
)
|
||||
def update_all_outputs(click_data, apply_clicks, reset_clicks, mode, clear_clicks,
|
||||
bt_end_date, time_factor, commission,
|
||||
start_date, end_date, current_selection):
|
||||
"""Main callback function that updates all dashboard components."""
|
||||
# Get trigger context
|
||||
ctx = dash.callback_context
|
||||
if not ctx.triggered:
|
||||
return get_default_outputs()
|
||||
|
||||
trigger_id = ctx.triggered[0]['prop_id'].split('.')[0]
|
||||
|
||||
# Handle mode changes and clear selection
|
||||
if trigger_id in ['dashboard-mode', 'strategy-clear-selection']:
|
||||
return handle_mode_change(trigger_id, mode, current_selection)
|
||||
|
||||
try:
|
||||
# Process date range and data
|
||||
start_date, end_date = get_date_range(trigger_id, start_date, end_date)
|
||||
data = get_filtered_data(start_date, end_date)
|
||||
commission = commission / 100
|
||||
|
||||
# Update strategy selection and get results
|
||||
current_selection = update_strategy_selection(mode, trigger_id, click_data, current_selection)
|
||||
result_dict = get_backtest_results(mode, current_selection, data, click_data, time_factor, commission)
|
||||
|
||||
# Create and return components
|
||||
return create_output_components(result_dict, mode, current_selection, start_date, end_date, click_data)
|
||||
|
||||
except ZeroDivisionError:
|
||||
return handle_error("zero_division", start_date, end_date, current_selection, mode)
|
||||
except Exception as e:
|
||||
print(f"Error in strategy details update: {str(e)}")
|
||||
return handle_error("general", start_date, end_date, current_selection, mode, str(e))
|
||||
@@ -0,0 +1,7 @@
|
||||
# callbacks/strategy_callbacks.py
|
||||
from .strategy.update_callback import register_update_callback
|
||||
# from .strategy.remove_callback import register_remove_callback
|
||||
|
||||
def register_strategy_callbacks(app):
|
||||
register_update_callback(app)
|
||||
# register_remove_callback(app)
|
||||
@@ -0,0 +1,5 @@
|
||||
# Configuration settings
|
||||
DEFAULT_TIME_FACTOR = 8760
|
||||
DEFAULT_COMMISSION = 0.06
|
||||
DEFAULT_WINDOW_RANGE = (100, 1000, 100)
|
||||
DEFAULT_THRESHOLD_RANGE = (0.0, 2.0, 0.2)
|
||||
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@@ -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'})
|
||||
@@ -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',
|
||||
'flexDirection': 'column'
|
||||
})
|
||||
], style={
|
||||
'display': 'flex',
|
||||
'marginTop': '20px'
|
||||
})
|
||||
])
|
||||
], style={
|
||||
'maxWidth': '1800px',
|
||||
'margin': '0 auto',
|
||||
'padding': '20px',
|
||||
'backgroundColor': '#ffffff'
|
||||
})
|
||||
])
|
||||
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|
||||
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.) [%]': ar,
|
||||
'Sharpe Ratio': sharpe,
|
||||
'Calmar Ratio': calmar,
|
||||
'Max. Drawdown [%]': mdd,
|
||||
'Beta': beta
|
||||
}
|
||||
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|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
def load_data(file_path='data/data.csv'):
|
||||
try:
|
||||
df = pd.read_csv(file_path)
|
||||
df['date'] = pd.to_datetime(df['date'])
|
||||
df.set_index('date', inplace=True)
|
||||
|
||||
required_columns = ['Open', 'Close', 'active_addresses']
|
||||
missing_columns = [col for col in required_columns if col not in df.columns]
|
||||
|
||||
if missing_columns:
|
||||
raise ValueError(f"Missing required columns: {missing_columns}")
|
||||
|
||||
return df
|
||||
|
||||
except FileNotFoundError:
|
||||
raise FileNotFoundError(f"{file_path} not found")
|
||||
except Exception as e:
|
||||
raise Exception(f"Error loading data: {str(e)}")
|
||||
|
||||
def get_default_date_range(df):
|
||||
"""Get the earliest and latest dates from the dataframe"""
|
||||
return df.index.min().strftime('%Y-%m-%d'), df.index.max().strftime('%Y-%m-%d')
|
||||
|
||||
|
||||
def zscore(x, window):
|
||||
return (x - x.rolling(window).mean()) / x.rolling(window).std()
|
||||
10
hkdse_maths_tutor/quotation1/notes.md
Normal file
10
hkdse_maths_tutor/quotation1/notes.md
Normal file
@@ -0,0 +1,10 @@
|
||||
---
|
||||
tags: python, dashboard
|
||||
---
|
||||
|
||||
主要想 de 一個 bug ，
|
||||
|
||||
加整多幾個
|
||||
|
||||
第一功能就係新增一個 correlation heatmap,
|
||||
另外就新增一個 export 功能，把我所選的數據 export 到 csv
|
||||
Reference in New Issue
Block a user