feat: HTML report generation for trade analysis
- Add _generate_html_report() function with self-contained HTML output - Charts built with direct Plotly.newPlot JSON (no pio.to_html conflicts) - Equity curve, drawdown, daily P&L, DOW, hour of day charts - Both view overlays original and edited equity curves - Stats grid with delta arrows in Both mode - Monthly performance table with JS $ / % toggle - Initial balance passed explicitly from session state at download time - Position summary and trade log as collapsible <details> dropdowns - Daily P&L x-axis labels hidden for readability - Download button at top of Overall mode, filename includes view and timestamp - Move Initial Balance input to filters row so it's set before report renders - Remove duplicate deposit input from monthly table section
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@@ -49,6 +49,345 @@ def _normalise_ic(df):
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return out
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def _generate_html_report(df_plot, stats, fmt, view_sel,
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stats_compare=None, df_compare=None,
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group_summary=None, date_from=None, date_to=None,
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deposit=10000.0):
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"""Generate a self-contained HTML report of the trade analysis."""
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import pandas as pd
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import json
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from datetime import datetime
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now = datetime.now().strftime('%Y-%m-%d %H:%M')
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title = f"Trade Analysis Report — {view_sel}"
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df_s = df_plot.copy()
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df_s['net_profit'] = pd.to_numeric(df_s['net_profit'], errors='coerce').fillna(0)
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df_s['close_time'] = pd.to_datetime(df_s['close_time'], errors='coerce')
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df_s = df_s.dropna(subset=['close_time']).sort_values('close_time').reset_index(drop=True)
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df_s['_cum'] = df_s['net_profit'].cumsum()
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df_s['_peak'] = df_s['_cum'].cummax()
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df_s['_dd'] = df_s['_cum'] - df_s['_peak']
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df_s['win'] = df_s['net_profit'] > 0
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if 'day_of_week' not in df_s.columns:
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df_s['day_of_week'] = df_s['close_time'].dt.day_name()
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if 'hour' not in df_s.columns:
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df_s['hour'] = df_s['close_time'].dt.hour
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LAYOUT_BASE = {
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'plot_bgcolor': 'rgba(20,20,30,1)',
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'paper_bgcolor': 'rgba(20,20,30,1)',
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'font': {'color': '#ccc', 'family': 'sans-serif'},
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'legend': {'bgcolor': 'rgba(0,0,0,0)', 'borderwidth': 0},
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'xaxis': {'gridcolor': 'rgba(128,128,128,0.15)'},
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'yaxis': {'gridcolor': 'rgba(128,128,128,0.15)', 'tickprefix': '$'},
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}
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def _chart(div_id, traces, layout_extra=None):
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layout = {**LAYOUT_BASE, **(layout_extra or {})}
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traces_json = json.dumps(traces)
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layout_json = json.dumps(layout)
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return (
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f'<div id="{div_id}" style="width:100%;height:{layout.get("height",300)}px"></div>\n'
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f'<script>Plotly.newPlot("{div_id}",{traces_json},{layout_json},'
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f'{{"responsive":true,"displayModeBar":false}});</script>'
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)
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# Equity
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eq_traces = [{
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'type': 'scatter', 'mode': 'lines', 'name': 'Equity',
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'x': df_s['close_time'].dt.strftime('%Y-%m-%d %H:%M:%S').tolist(),
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'y': df_s['_cum'].round(2).tolist(),
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'line': {'color': '#7c6af7', 'width': 2},
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'fill': 'tozeroy', 'fillcolor': 'rgba(124,106,247,0.08)',
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}]
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if df_compare is not None:
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dc = df_compare.copy()
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dc['net_profit'] = pd.to_numeric(dc['net_profit'], errors='coerce').fillna(0)
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dc['close_time'] = pd.to_datetime(dc['close_time'], errors='coerce')
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dc = dc.dropna(subset=['close_time']).sort_values('close_time')
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dc['_cum'] = dc['net_profit'].cumsum()
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eq_traces.append({
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'type': 'scatter', 'mode': 'lines', 'name': 'Edited',
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'x': dc['close_time'].dt.strftime('%Y-%m-%d %H:%M:%S').tolist(),
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'y': dc['_cum'].round(2).tolist(),
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'line': {'color': '#34C27A', 'width': 2, 'dash': 'dash'},
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})
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eq_html = _chart('eq_chart', eq_traces, {'height': 300, 'title': 'Equity Curve',
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'hovermode': 'x unified', 'margin': {'l':60,'r':20,'t':40,'b':40}})
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# Drawdown
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dd_html = _chart('dd_chart', [{
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'type': 'scatter', 'mode': 'lines', 'name': 'Drawdown',
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'x': df_s['close_time'].dt.strftime('%Y-%m-%d %H:%M:%S').tolist(),
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'y': df_s['_dd'].round(2).tolist(),
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'line': {'color': '#dc5050', 'width': 1.5},
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'fill': 'tozeroy', 'fillcolor': 'rgba(220,80,80,0.18)',
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}], {'height': 150, 'title': 'Drawdown', 'showlegend': False,
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'margin': {'l':60,'r':20,'t':40,'b':20},
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'xaxis': {'gridcolor':'rgba(128,128,128,0.15)', 'showticklabels': False},
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'yaxis': {'gridcolor':'rgba(128,128,128,0.15)', 'tickprefix':'$'},
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'plot_bgcolor':'rgba(20,20,30,1)', 'paper_bgcolor':'rgba(20,20,30,1)',
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'font':{'color':'#ccc','family':'sans-serif'}})
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# Daily P&L
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daily = df_s.groupby(df_s['close_time'].dt.strftime('%Y-%m-%d'))['net_profit'].sum()
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daily_dates = daily.index.tolist()
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daily_vals = daily.round(2).tolist()
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daily_colors = ['rgba(52,194,122,0.85)' if v >= 0 else 'rgba(220,80,80,0.85)' for v in daily_vals]
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daily_html = _chart('daily_chart', [{
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'type': 'bar', 'name': 'Daily P&L',
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'x': daily_dates, 'y': daily_vals,
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'marker': {'color': daily_colors},
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}], {'height': 160, 'title': 'Daily P&L', 'showlegend': False,
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'bargap': 0.2, 'margin': {'l':60,'r':20,'t':40,'b':40},
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'xaxis': {'type': 'category', 'gridcolor': 'rgba(128,128,128,0.15)', 'showticklabels': False},
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'yaxis': {'gridcolor': 'rgba(128,128,128,0.15)', 'tickprefix': '$',
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'zeroline': True, 'zerolinecolor': 'rgba(128,128,128,0.4)'},
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'plot_bgcolor':'rgba(20,20,30,1)', 'paper_bgcolor':'rgba(20,20,30,1)',
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'font':{'color':'#ccc','family':'sans-serif'}})
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# DOW
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dow_order = ['Monday','Tuesday','Wednesday','Thursday','Friday']
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present_days = [d for d in dow_order if d in df_s['day_of_week'].values]
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wins_dow = df_s[df_s['win']].groupby('day_of_week')['net_profit'].sum().reindex(present_days, fill_value=0)
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losses_dow = df_s[~df_s['win']].groupby('day_of_week')['net_profit'].sum().reindex(present_days, fill_value=0)
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dow_html = _chart('dow_chart', [
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{'type':'bar','name':'Profit','x':present_days,'y':wins_dow.round(2).tolist(),
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'marker':{'color':'rgba(52,194,122,0.85)'}},
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{'type':'bar','name':'Loss', 'x':present_days,'y':losses_dow.round(2).tolist(),
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'marker':{'color':'rgba(220,80,80,0.85)'}},
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], {'height':280,'title':'P&L by Day of Week','barmode':'relative','bargap':0.3,
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'margin':{'l':60,'r':20,'t':40,'b':40},
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'xaxis':{'type':'category','gridcolor':'rgba(128,128,128,0.15)'},
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'yaxis':{'gridcolor':'rgba(128,128,128,0.15)','tickprefix':'$'},
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'plot_bgcolor':'rgba(20,20,30,1)','paper_bgcolor':'rgba(20,20,30,1)',
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'font':{'color':'#ccc','family':'sans-serif'},
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'legend':{'bgcolor':'rgba(0,0,0,0)'}})
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# Hour
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all_hours = sorted(df_s['hour'].unique())
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str_hours = [str(h) for h in all_hours]
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wins_h = df_s[df_s['win']].groupby('hour')['net_profit'].sum().reindex(all_hours, fill_value=0)
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losses_h = df_s[~df_s['win']].groupby('hour')['net_profit'].sum().reindex(all_hours, fill_value=0)
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hour_html = _chart('hour_chart', [
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{'type':'bar','name':'Profit','x':str_hours,'y':wins_h.round(2).tolist(),
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'marker':{'color':'rgba(52,194,122,0.85)'}},
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{'type':'bar','name':'Loss', 'x':str_hours,'y':losses_h.round(2).tolist(),
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'marker':{'color':'rgba(220,80,80,0.85)'}},
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], {'height':280,'title':'P&L by Hour of Day','barmode':'relative','bargap':0.3,
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'margin':{'l':60,'r':20,'t':40,'b':40},
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'xaxis':{'type':'category','title':'Hour (UTC)','gridcolor':'rgba(128,128,128,0.15)'},
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'yaxis':{'gridcolor':'rgba(128,128,128,0.15)','tickprefix':'$'},
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'plot_bgcolor':'rgba(20,20,30,1)','paper_bgcolor':'rgba(20,20,30,1)',
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'font':{'color':'#ccc','family':'sans-serif'},
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'legend':{'bgcolor':'rgba(0,0,0,0)'}})
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# ── Stats table ───────────────────────────────────────────────────────────
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def _delta_html(key, fmt='$', inverse=False):
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if stats_compare is None or key not in stats_compare: return ''
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diff = stats_compare[key] - stats[key]
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if abs(diff) < 0.001: return ''
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better = diff > 0 if not inverse else diff < 0
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col = '#34C27A' if better else '#E05555'
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arrow = '▲' if diff > 0 else '▼'
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val = f"${abs(diff):.2f}" if fmt=='$' else f"{abs(diff):.2f}"
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return f'<span style="color:{col};font-size:11px;margin-left:6px">{arrow}{val}</span>'
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def _stat(label, val, delta=''):
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return f'<div class="sc"><div class="sl">{label}</div><div class="sv">{val}{delta}</div></div>'
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stats_html = f"""
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<div class="stats-grid">
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{_stat("Net Profit", f"${stats['net_profit']:,.2f}", _delta_html('net_profit','$'))}
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{_stat("Win Rate", f"{stats['win_rate']}%", _delta_html('win_rate','%'))}
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{_stat("Profit Factor", str(stats['profit_factor']), _delta_html('profit_factor','x'))}
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{_stat("R:R Ratio", str(stats['rr_ratio']), _delta_html('rr_ratio','x'))}
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{_stat("Expectancy", f"${stats['expectancy']:,.2f}", _delta_html('expectancy','$'))}
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{_stat("Total Trades", str(stats['total_trades']), _delta_html('total_trades',''))}
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{_stat("Trading Days", str(stats.get('trading_days',0)), _delta_html('trading_days',''))}
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{_stat("Trades/Day", str(stats.get('trades_per_day',0)), _delta_html('trades_per_day','x'))}
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{_stat("Avg Win", f"${stats['avg_win']:,.2f}", _delta_html('avg_win','$'))}
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{_stat("Avg Loss", f"${stats['avg_loss']:,.2f}", _delta_html('avg_loss','$', inverse=True))}
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{_stat("Max DD", f"${stats['max_drawdown']:,.2f}", _delta_html('max_drawdown','$', inverse=True))}
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{_stat("Best Trade", f"${stats['best_trade']:,.2f}", _delta_html('best_trade','$'))}
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{_stat("Worst Trade",f"${stats['worst_trade']:,.2f}", _delta_html('worst_trade','$', inverse=True))}
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{_stat("Max Consec Wins", str(stats['max_consec_wins']), _delta_html('max_consec_wins',''))}
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{_stat("Max Consec Losses", str(stats['max_consec_losses']), _delta_html('max_consec_losses','', inverse=True))}
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{_stat("Long Trades", str(stats['long_trades']), _delta_html('long_trades',''))}
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{_stat("Long Win Rate",f"{stats['long_win_rate']}%", _delta_html('long_win_rate','%'))}
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{_stat("Short Trades", str(stats['short_trades']), _delta_html('short_trades',''))}
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{_stat("Short Win Rate",f"{stats['short_win_rate']}%", _delta_html('short_win_rate','%'))}
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</div>"""
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# ── Monthly table ─────────────────────────────────────────────────────────
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def _monthly_html(df_m, label, deposit=10000.0, table_id='mt1'):
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if df_m is None or df_m.empty: return ''
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tmp = df_m[['close_time','net_profit']].dropna().copy()
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tmp['year'] = pd.to_datetime(tmp['close_time']).dt.year
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tmp['month'] = pd.to_datetime(tmp['close_time']).dt.month
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monthly = tmp.groupby(['year','month'])['net_profit'].sum().reset_index()
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if monthly.empty: return ''
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pivot = monthly.pivot(index='year', columns='month', values='net_profit').fillna(0)
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pivot.columns = [pd.Timestamp(2000,int(m),1).strftime('%b') for m in pivot.columns]
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pivot['YTD'] = pivot.sum(axis=1)
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pivot = pivot.sort_index(ascending=False)
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month_order = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec','YTD']
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cols = [c for c in month_order if c in pivot.columns]
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hdr = '<tr><th>Year</th>' + ''.join(f'<th>{c}</th>' for c in cols) + '</tr>'
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def _rows(use_pct):
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out = ''
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for year, row in pivot[cols].iterrows():
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cells = f'<td>{year}</td>'
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for col in cols:
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v = row.get(col, 0)
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pv = round(v / deposit * 100, 2) if use_pct else v
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bg = 'rgba(52,194,122,0.18)' if pv>0 else ('rgba(220,80,80,0.18)' if pv<0 else 'transparent')
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fg = '#34C27A' if pv>0 else ('#E05555' if pv<0 else '#888')
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txt = (f'{pv:+.2f}%' if pv!=0 else '—') if use_pct else (f'{pv:+.2f}' if pv!=0 else '—')
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cells += f'<td style="background:{bg};color:{fg}">{txt}</td>'
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out += f'<tr>{cells}</tr>'
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return out
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rows_d = _rows(False)
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rows_p = _rows(True)
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return f'''
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<div style="margin:20px 0">
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<div style="display:flex;align-items:center;gap:12px;margin-bottom:8px">
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<h3 style="margin:0">{label}</h3>
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<div style="display:flex;border:1px solid rgba(255,255,255,0.15);border-radius:4px;overflow:hidden;font-size:11px">
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<button onclick="mtToggle('{table_id}','$')" id="{table_id}_btn_d"
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style="padding:3px 10px;background:rgba(124,106,247,0.3);color:#e2e8f0;border:none;cursor:pointer">$</button>
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<button onclick="mtToggle('{table_id}','%')" id="{table_id}_btn_p"
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style="padding:3px 10px;background:transparent;color:#888;border:none;cursor:pointer">%</button>
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</div>
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<span style="font-size:11px;color:#666">Initial balance: ${deposit:,.0f}</span>
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</div>
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<div id="{table_id}_d"><table class="tbl"><thead>{hdr}</thead><tbody>{rows_d}</tbody></table></div>
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<div id="{table_id}_p" style="display:none"><table class="tbl"><thead>{hdr}</thead><tbody>{rows_p}</tbody></table></div>
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</div>
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<script>
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function mtToggle(id, mode) {{
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document.getElementById(id+'_d').style.display = mode==='$' ? '' : 'none';
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document.getElementById(id+'_p').style.display = mode==='%' ? '' : 'none';
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document.getElementById(id+'_btn_d').style.background = mode==='$' ? 'rgba(124,106,247,0.3)' : 'transparent';
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document.getElementById(id+'_btn_d').style.color = mode==='$' ? '#e2e8f0' : '#888';
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document.getElementById(id+'_btn_p').style.background = mode==='%' ? 'rgba(124,106,247,0.3)' : 'transparent';
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document.getElementById(id+'_btn_p').style.color = mode==='%' ? '#e2e8f0' : '#888';
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}}
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</script>'''
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monthly_html = _monthly_html(df_s, "Monthly Performance", deposit=deposit, table_id='mt1')
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# ── Position summary ──────────────────────────────────────────────────────
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pos_html = ''
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if group_summary:
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gs_df = pd.DataFrame(group_summary)
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hdr = '<tr>' + ''.join(f'<th>{c}</th>' for c in gs_df.columns) + '</tr>'
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rows_html = ''
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for _, row in gs_df.iterrows():
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v = row.get('Net P&L ($)', 0)
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try: v = float(v)
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except: v = 0
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bg = 'rgba(52,194,122,0.12)' if v>0 else ('rgba(220,80,80,0.12)' if v<0 else '')
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cells = ''.join(f'<td style="background:{bg if col=='Net P&L ($)' else ''}">{row[col]}</td>'
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for col in gs_df.columns)
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rows_html += f'<tr>{cells}</tr>'
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n_pos = len(gs_df)
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pos_html = (
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f'<details class="sd"><summary>Position Summary ({n_pos} positions)</summary>'
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f'<table class="tbl"><thead>{hdr}</thead><tbody>{rows_html}</tbody></table>'
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f'</details>'
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)
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# ── Trade log ─────────────────────────────────────────────────────────────
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log_cols = ['open_time','close_time','symbol','type','volume',
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'open_price','close_price','net_profit']
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log_cols = [c for c in log_cols if c in df_s.columns]
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log_hdr = '<tr>' + ''.join(f'<th>{c}</th>' for c in log_cols) + '</tr>'
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log_rows = ''
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for i, (_, row) in enumerate(df_s[log_cols].iterrows()):
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bg = ''
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try:
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v = float(row['net_profit'])
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bg = 'rgba(52,194,122,0.08)' if v>0 else 'rgba(220,80,80,0.08)'
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except: pass
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cells = ''.join(f'<td style="background:{bg if col=='net_profit' else ''}">{row[col]}</td>'
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for col in log_cols)
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log_rows += f'<tr>{cells}</tr>'
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n_log = len(df_s)
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log_html = (
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f'<details class="sd"><summary>Trade Log ({n_log} trades)</summary>'
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f'<table class="tbl tbl-sm"><thead>{log_hdr}</thead><tbody>{log_rows}</tbody></table>'
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f'</details>'
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)
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# ── Assemble ──────────────────────────────────────────────────────────────
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date_str = f"{date_from} — {date_to}" if date_from else ''
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html = f"""<!DOCTYPE html>
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<html><head>
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<meta charset="utf-8">
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<title>{title}</title>
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<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
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<style>
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* {{ box-sizing: border-box; margin: 0; padding: 0; }}
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body {{ background:#0e1117; color:#e2e8f0; font-family:sans-serif; font-size:13px; padding:24px; }}
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h1 {{ font-size:22px; color:#7c6af7; margin-bottom:4px; }}
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h2 {{ font-size:16px; color:#a0aec0; margin:24px 0 12px; border-bottom:1px solid rgba(255,255,255,0.08); padding-bottom:6px; }}
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h3 {{ font-size:14px; color:#a0aec0; margin:20px 0 8px; }}
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.meta {{ color:#666; font-size:11px; margin-bottom:24px; }}
|
||||
.stats-grid {{ display:grid; grid-template-columns:repeat(5,1fr); gap:10px; margin-bottom:20px; }}
|
||||
.sc {{ background:rgba(255,255,255,0.04); border-radius:6px; padding:10px 12px; }}
|
||||
.sl {{ font-size:10px; color:#888; margin-bottom:4px; text-transform:uppercase; letter-spacing:.5px; }}
|
||||
.sv {{ font-size:16px; font-weight:600; color:#e2e8f0; }}
|
||||
.charts {{ display:grid; grid-template-columns:1fr 1fr; gap:16px; margin-bottom:20px; }}
|
||||
.chart-full {{ margin-bottom:8px; }}
|
||||
table.tbl {{ width:100%; border-collapse:collapse; font-size:12px; margin-bottom:20px; }}
|
||||
table.tbl th {{ background:rgba(255,255,255,0.06); padding:6px 10px; text-align:left; color:#888; font-weight:500; }}
|
||||
table.tbl td {{ padding:5px 10px; border-bottom:1px solid rgba(255,255,255,0.04); }}
|
||||
table.tbl-sm td, table.tbl-sm th {{ font-size:11px; padding:3px 8px; }}
|
||||
.tag {{ display:inline-block; background:rgba(124,106,247,0.2); color:#7c6af7;
|
||||
border-radius:4px; padding:2px 8px; font-size:11px; margin-bottom:16px; }}
|
||||
details.sd {{ margin:20px 0; border:1px solid rgba(255,255,255,0.08); border-radius:6px; overflow:hidden; }}
|
||||
details.sd summary {{ padding:10px 16px; cursor:pointer; font-size:14px; font-weight:600;
|
||||
color:#a0aec0; background:rgba(255,255,255,0.03); list-style:none;
|
||||
display:flex; align-items:center; gap:8px; user-select:none; }}
|
||||
details.sd summary::-webkit-details-marker {{ display:none; }}
|
||||
details.sd summary::before {{ content:'\25B6'; font-size:10px; transition:transform 0.2s; }}
|
||||
details[open].sd summary::before {{ transform:rotate(90deg); }}
|
||||
details.sd summary:hover {{ background:rgba(255,255,255,0.06); }}
|
||||
</style>
|
||||
</head><body>
|
||||
<h1>{title}</h1>
|
||||
<div class="meta">Generated {now} · {date_str} · Format: {fmt}</div>
|
||||
<span class="tag">{view_sel}</span>
|
||||
|
||||
<h2>Statistics</h2>
|
||||
{stats_html}
|
||||
|
||||
<h2>Charts</h2>
|
||||
<div class="chart-full">{eq_html}</div>
|
||||
<div class="chart-full">{dd_html}</div>
|
||||
<div class="chart-full">{daily_html}</div>
|
||||
<div class="charts">
|
||||
<div>{dow_html}</div>
|
||||
<div>{hour_html}</div>
|
||||
</div>
|
||||
|
||||
{pos_html}
|
||||
|
||||
{monthly_html}
|
||||
|
||||
<h2>Trade Log</h2>
|
||||
{log_html}
|
||||
|
||||
</body></html>"""
|
||||
return html
|
||||
|
||||
|
||||
def render():
|
||||
st.title("📊 Trade Analysis")
|
||||
|
||||
@@ -173,7 +512,7 @@ def render():
|
||||
if 'ta_deposit' not in st.session_state:
|
||||
st.session_state['ta_deposit'] = 10000.0
|
||||
|
||||
fc1, fc2, fc3, fc4 = st.columns(4)
|
||||
fc1, fc2, fc3, fc4, fc5 = st.columns(5)
|
||||
|
||||
with fc1:
|
||||
valid_times = df_all['open_time'].dropna()
|
||||
@@ -200,6 +539,13 @@ def render():
|
||||
sel_trades = st.multiselect("Trade #", trade_nums, key='ta_idx_sel',
|
||||
placeholder="All trades (filter by #)")
|
||||
|
||||
with fc5:
|
||||
st.session_state['ta_deposit'] = st.number_input(
|
||||
"Initial Balance ($)", min_value=100.0, max_value=10_000_000.0,
|
||||
value=st.session_state.get('ta_deposit', 10000.0),
|
||||
step=1000.0, format="%.0f", key='ta_deposit_filter',
|
||||
help="Used for % calculations in monthly table and report")
|
||||
|
||||
|
||||
# Apply filters
|
||||
def _apply_filters(src_df):
|
||||
@@ -539,17 +885,8 @@ def render():
|
||||
pivot['YTD'] = pivot.sum(axis=1)
|
||||
pivot = pivot.sort_index(ascending=False)
|
||||
|
||||
# Deposit for % calc — use initial deposit from session state or fallback to first equity point
|
||||
deposit = st.session_state.get('ta_deposit', 10000.0)
|
||||
|
||||
tog1, tog2 = st.columns([2, 3])
|
||||
toggle = tog1.radio("Unit", ["$", "%"], horizontal=True, key=f"{key_prefix}_toggle")
|
||||
deposit = tog2.number_input(
|
||||
"Initial Balance ($)", min_value=100.0, max_value=10_000_000.0,
|
||||
value=st.session_state.get('ta_deposit', 10000.0),
|
||||
step=1000.0, format="%.2f", key=f"{key_prefix}_deposit",
|
||||
help="Used for % calculations")
|
||||
st.session_state['ta_deposit'] = deposit
|
||||
toggle = st.radio("Unit", ["$", "%"], horizontal=True, key=f"{key_prefix}_toggle")
|
||||
|
||||
month_order = ['Jan','Feb','Mar','Apr','May','Jun',
|
||||
'Jul','Aug','Sep','Oct','Nov','Dec','YTD']
|
||||
@@ -602,6 +939,34 @@ def render():
|
||||
if mode == "Overall":
|
||||
stats = calc_stats(df)
|
||||
stats_e = calc_stats(df_e) if df_e is not None else None
|
||||
|
||||
# ── Report download ───────────────────────────────────────────────
|
||||
_rep_df = df_e if (view_sel in ("Edited","Both") and df_e is not None) else df
|
||||
_rep_stats = stats_e if (view_sel == "Edited" and stats_e) else stats
|
||||
_rep_cmp_s = stats_e if (view_sel == "Both" and stats_e) else None
|
||||
_rep_cmp_d = df_e if (view_sel == "Both" and df_e is not None) else None
|
||||
_rep_grp = st.session_state.get('ta_group_summary') if view_sel in ("Edited","Both") else None
|
||||
try:
|
||||
from datetime import datetime as _dt
|
||||
_rep_html = _generate_html_report(
|
||||
_rep_df, _rep_stats, fmt or '', view_sel,
|
||||
stats_compare=_rep_cmp_s, df_compare=_rep_cmp_d,
|
||||
group_summary=_rep_grp,
|
||||
date_from=str(date_from), date_to=str(date_to),
|
||||
deposit=st.session_state.get('ta_deposit', 10000.0),
|
||||
)
|
||||
st.download_button(
|
||||
"📄 Download HTML Report",
|
||||
data = _rep_html,
|
||||
file_name = f"trade_report_{view_sel.lower()}_{_dt.now().strftime('%Y%m%d_%H%M')}.html",
|
||||
mime = 'text/html',
|
||||
key = 'ta_report_dl',
|
||||
)
|
||||
except Exception as _e:
|
||||
import traceback
|
||||
st.error(f"Report generation error: {_e}")
|
||||
st.code(traceback.format_exc())
|
||||
|
||||
if view_sel == "Edited" and stats_e:
|
||||
render_stats(stats_e, "Overall Statistics (Edited)")
|
||||
elif view_sel == "Both" and stats_e:
|
||||
@@ -877,10 +1242,16 @@ def render():
|
||||
summary_rows = []
|
||||
grp_labels = upd_with_groups['Group'].fillna('').str.strip()
|
||||
|
||||
# Add 1-based index to upd_with_groups for trade # reference
|
||||
upd_with_groups = upd_with_groups.reset_index(drop=True)
|
||||
upd_with_groups['_idx'] = range(1, len(upd_with_groups) + 1)
|
||||
|
||||
# Grouped trades first
|
||||
for label, grp in upd_with_groups[grp_labels != ''].groupby(grp_labels[grp_labels != '']):
|
||||
net = grp['net_profit'].sum()
|
||||
net = grp['net_profit'].sum()
|
||||
trade_nums = ', '.join(str(i) for i in sorted(grp['_idx'].tolist()))
|
||||
summary_rows.append({
|
||||
'Trade #': trade_nums,
|
||||
'Group': label,
|
||||
'Entries': len(grp),
|
||||
'Symbol': grp['symbol'].iloc[0],
|
||||
@@ -896,6 +1267,7 @@ def render():
|
||||
for _, row in upd_with_groups[grp_labels == ''].iterrows():
|
||||
net = row['net_profit']
|
||||
summary_rows.append({
|
||||
'Trade #': str(int(row['_idx'])),
|
||||
'Group': '—',
|
||||
'Entries': 1,
|
||||
'Symbol': row['symbol'],
|
||||
|
||||
Reference in New Issue
Block a user