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feat: trade analysis overhaul — editing, grouping, comparison views

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Trade Analysis:
- Add editable raw trade log with # index and Group column
- Group column merges multiple entries into one position on Update
- Reset button clears edited version back to original upload
- View selector: Original / Edited / Both — appears after first Update
- Both mode: stats show with coloured delta arrows vs edited version
- Both mode: equity, drawdown and daily P&L charts overlay both versions
- Both mode: strategy comparison table shows inline delta values
- Position Summary expander above monthly table — all positions listed
  (grouped with entry count, individual as single rows) sorted by open time
- Download exports edited version when available, original otherwise
- Trade # multiselect filter to show specific trades
- All charts and tables (DOW, hour, monthly) respect view selection
- Fix: Group column positioned second after # index
- Fix: DataFrame truth value error in download button
- Fix: Both mode now uses edited df for DOW/hour/monthly charts

mt5_parser:
- Add trading_days and trades_per_day to calc_stats
- Add ISO datetime format fallback for CSV imports
- Fix missing comment column KeyError in _enrich
- Fix NaT in date filter when open_time has null values
This commit is contained in:
unknown
2026-04-18 08:54:02 +10:00
parent 413d9bb3f4
commit fa5ceca982
5 changed files with 470 additions and 112 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.streamlit/config.toml
+5 -5
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@@ -1,7 +1,7 @@
[theme]
base = "light"
primaryColor = "#2E75B6"
backgroundColor = "#ffffff"
secondaryBackgroundColor = "#f0f2f6"
textColor = "#1a1a1a"
base = "dark"
primaryColor = "#7c6af7"
backgroundColor = "#0e1117"
secondaryBackgroundColor = "#1a1f2e"
textColor = "#fafafa"
font = "sans serif"
__pycache__/mt5_parser.cpython-314.pyc
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__pycache__/view_trade_analysis.cpython-314.pyc
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mt5_parser.py
+5
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@@ -350,8 +350,13 @@ def calc_stats(df):
longs = df[df['type'] == 'buy']
shorts = df[df['type'] == 'sell']
trading_days = df['open_time'].dt.date.nunique() if 'open_time' in df.columns else 0
trades_per_day = round(total / trading_days, 2) if trading_days > 0 else 0
return {
'total_trades' : total,
'trading_days' : trading_days,
'trades_per_day' : trades_per_day,
'win_rate' : win_rate,
'net_profit' : net_profit,
'gross_profit' : gross_profit,
view_trade_analysis.py
+460 -107
View File
@@ -54,8 +54,11 @@ def render():
# ── Session state ─────────────────────────────────────────────────────────
for _k, _v in {
'ta_df': None, 'ta_format': None,
'ta_accounts': [], 'ta_ic_bytes': None,
'ta_df': None, 'ta_format': None,
'ta_accounts': [], 'ta_ic_bytes': None,
'ta_df_original': None,
'ta_df_edited': None,
'ta_group_summary': None,
}.items():
if _k not in st.session_state:
st.session_state[_k] = _v
@@ -85,9 +88,10 @@ def render():
if uploaded and uploaded.name.lower().endswith(('.htm','.html','.csv')):
df, fmt = detect_and_parse(uploaded.read(), uploaded.name)
if df is not None:
st.session_state['ta_df'] = df
st.session_state['ta_format'] = fmt
st.session_state['ta_accounts'] = []
st.session_state['ta_df'] = df
st.session_state['ta_df_original'] = df.copy()
st.session_state['ta_format'] = fmt
st.session_state['ta_accounts'] = []
st.success(f"✓ Loaded {len(df)} trades — {fmt}")
else:
st.error("Could not parse report — check file format")
@@ -117,7 +121,8 @@ def render():
st.session_state['ta_format'] = "IC Markets XLSX"
df_ic = parse_icmarkets_xlsx(file_bytes, account=accounts[0])
df_ic = _normalise_ic(df_ic)
st.session_state['ta_df'] = df_ic
st.session_state['ta_df'] = df_ic
st.session_state['ta_df_original'] = df_ic.copy()
st.success(f"✓ Loaded {len(df_ic)} trades — {len(accounts)} account(s) found")
except Exception as e:
st.error(f"Error parsing file: {e}")
@@ -138,8 +143,9 @@ def render():
df_ic = _normalise_ic(df_ic)
st.session_state['ta_df'] = df_ic
df_all = st.session_state['ta_df']
fmt = st.session_state['ta_format']
df_all = st.session_state['ta_df']
df_edited = st.session_state.get('ta_df_edited')
fmt = st.session_state['ta_format']
if df_all is None or len(df_all) == 0:
st.markdown("""
@@ -153,6 +159,15 @@ def render():
if fmt:
st.caption(f"Format detected: **{fmt}** · {len(df_all)} total trades")
# ── View selector ─────────────────────────────────────────────────────────
has_edited = st.session_state.get('ta_df_edited') is not None
if has_edited:
view_opts = ["Original", "Edited", "Both"]
view_sel = st.radio("View", view_opts, horizontal=True, key='ta_view_sel')
else:
view_sel = "Original"
st.session_state['ta_view_sel'] = "Original"
# ── Filters ───────────────────────────────────────────────────────────────
st.divider()
if 'ta_deposit' not in st.session_state:
@@ -181,20 +196,30 @@ def render():
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
sel_days = st.multiselect("Day of week", days, key='ta_days')
sel_type = st.multiselect("Type", ['buy', 'sell'], key='ta_type')
trade_nums = [str(i) for i in range(1, len(df_all)+1)]
sel_trades = st.multiselect("Trade #", trade_nums, key='ta_idx_sel',
placeholder="All trades (filter by #)")
# Apply filters
df = df_all.copy()
df = df[(df['open_time'].dt.date >= date_from) &
(df['open_time'].dt.date <= date_to)]
if sel_symbol:
df = df[df['symbol'].isin(sel_symbol)]
if sel_strategy:
df = df[df['strategy'].isin(sel_strategy)]
if sel_days:
df = df[df['day_of_week'].isin(sel_days)]
if sel_type:
df = df[df['type'].isin(sel_type)]
def _apply_filters(src_df):
d = src_df.copy()
d = d[(d['open_time'].dt.date >= date_from) &
(d['open_time'].dt.date <= date_to)]
if sel_symbol: d = d[d['symbol'].isin(sel_symbol)]
if sel_strategy: d = d[d['strategy'].isin(sel_strategy)]
if sel_days: d = d[d['day_of_week'].isin(sel_days)]
if sel_type: d = d[d['type'].isin(sel_type)]
d = d.reset_index(drop=True)
if sel_trades:
sel_idx = [int(t)-1 for t in sel_trades if int(t)-1 < len(d)]
d = d.iloc[sel_idx].reset_index(drop=True)
return d
df = _apply_filters(df_all)
# Also prepare edited df if available
df_e = _apply_filters(df_edited) if df_edited is not None else None
st.caption(f"Showing **{len(df)}** trades after filters")
@@ -207,38 +232,93 @@ def render():
st.divider()
# ── Helpers ───────────────────────────────────────────────────────────────
def render_stats(stats, label=""):
def render_stats(stats, label="", stats_compare=None):
if label:
st.markdown(f"**{label}**")
c1, c2, c3, c4, c5 = st.columns(5)
c1.metric("Net Profit", f"${stats['net_profit']:,.2f}")
c2.metric("Win Rate", f"{stats['win_rate']}%")
c3.metric("Profit Factor", f"{stats['profit_factor']}")
c4.metric("R:R Ratio", f"{stats['rr_ratio']}")
c5.metric("Expectancy", f"${stats['expectancy']:,.2f}")
def _delta(key, fmt='$', higher_is_better=True):
"""Return delta string for st.metric when compare stats available."""
if stats_compare is None or key not in stats_compare:
return None
diff = stats_compare[key] - stats[key]
if diff == 0:
return None
if fmt == '$':
return f"${diff:+,.2f}"
elif fmt == '%':
return f"{diff:+.1f}%"
elif fmt == 'x':
return f"{diff:+.2f}"
else:
return f"{diff:+g}"
def _inv_delta(key, fmt='$'):
"""Delta where lower is better (e.g. drawdown, losses)."""
if stats_compare is None or key not in stats_compare:
return None
diff = stats_compare[key] - stats[key]
if diff == 0:
return None
if fmt == '$':
return f"${diff:+,.2f}"
elif fmt == '%':
return f"{diff:+.1f}%"
else:
return f"{diff:+g}"
c1, c2, c3, c4, c5 = st.columns(5)
c1.metric("Total Trades", stats['total_trades'])
c2.metric("Avg Win", f"${stats['avg_win']:,.2f}")
c3.metric("Avg Loss", f"${stats['avg_loss']:,.2f}")
c4.metric("Max DD", f"${stats['max_drawdown']:,.2f}")
c5.metric("Best Trade", f"${stats['best_trade']:,.2f}")
c1.metric("Net Profit", f"${stats['net_profit']:,.2f}",
delta=_delta('net_profit','$'))
c2.metric("Win Rate", f"{stats['win_rate']}%",
delta=_delta('win_rate','%'))
c3.metric("Profit Factor", f"{stats['profit_factor']}",
delta=_delta('profit_factor','x'))
c4.metric("R:R Ratio", f"{stats['rr_ratio']}",
delta=_delta('rr_ratio','x'))
c5.metric("Expectancy", f"${stats['expectancy']:,.2f}",
delta=_delta('expectancy','$'))
c1, c2, c3, c4, c5 = st.columns(5)
c1.metric("Max Consec Wins", stats['max_consec_wins'])
c2.metric("Max Consec Losses", stats['max_consec_losses'])
c3.metric("Avg Win Dur", f"{stats['avg_win_duration']}m")
c4.metric("Avg Loss Dur", f"{stats['avg_loss_duration']}m")
c5.metric("Worst Trade", f"${stats['worst_trade']:,.2f}")
c1.metric("Total Trades", stats['total_trades'],
delta=_delta('total_trades',''))
c2.metric("Avg Win", f"${stats['avg_win']:,.2f}",
delta=_delta('avg_win','$'))
c3.metric("Avg Loss", f"${stats['avg_loss']:,.2f}",
delta=_inv_delta('avg_loss','$'), delta_color="inverse")
c4.metric("Max DD", f"${stats['max_drawdown']:,.2f}",
delta=_inv_delta('max_drawdown','$'), delta_color="inverse")
c5.metric("Best Trade", f"${stats['best_trade']:,.2f}",
delta=_delta('best_trade','$'))
c1, c2, c3, c4, c5 = st.columns(5)
c1.metric("Max Consec Wins", stats['max_consec_wins'],
delta=_delta('max_consec_wins',''))
c2.metric("Max Consec Losses", stats['max_consec_losses'],
delta=_inv_delta('max_consec_losses',''), delta_color="inverse")
c3.metric("Avg Win Dur", f"{stats['avg_win_duration']}m",
delta=_delta('avg_win_duration',''))
c4.metric("Avg Loss Dur", f"{stats['avg_loss_duration']}m",
delta=_inv_delta('avg_loss_duration',''), delta_color="inverse")
c5.metric("Worst Trade", f"${stats['worst_trade']:,.2f}",
delta=_inv_delta('worst_trade','$'), delta_color="inverse")
c1, c2, c3, c4 = st.columns(4)
c1.metric("Long Trades", stats['long_trades'])
c2.metric("Long Win Rate", f"{stats['long_win_rate']}%")
c3.metric("Short Trades", stats['short_trades'])
c4.metric("Short Win Rate",f"{stats['short_win_rate']}%")
c1.metric("Long Trades", stats['long_trades'],
delta=_delta('long_trades',''))
c2.metric("Long Win Rate", f"{stats['long_win_rate']}%",
delta=_delta('long_win_rate','%'))
c3.metric("Short Trades", stats['short_trades'],
delta=_delta('short_trades',''))
c4.metric("Short Win Rate",f"{stats['short_win_rate']}%",
delta=_delta('short_win_rate','%'))
def render_equity_curve(df_plot, label="Equity Curve"):
c1, c2, c3, c4 = st.columns(4)
c1.metric("Trading Days", stats.get('trading_days', 0),
delta=_delta('trading_days',''))
c2.metric("Trades / Day", f"{stats.get('trades_per_day', 0)}",
delta=_delta('trades_per_day','x'))
def render_equity_curve(df_plot, label="Equity Curve", df_compare=None, compare_label="Edited"):
import pandas as pd
df_s = df_plot.sort_values('close_time').copy()
@@ -257,10 +337,20 @@ def render():
if show_account:
df_s['_cum'] = df_s['net_profit'].cumsum()
fig.add_trace(go.Scatter(
x=df_s['close_time'], y=df_s['_cum'], mode='lines', name='Account',
x=df_s['close_time'], y=df_s['_cum'], mode='lines', name='Original',
line=dict(color='#7c6af7', width=2),
fill='tozeroy', fillcolor='rgba(124,106,247,0.06)',
))
# Overlay edited/compare line if provided
if df_compare is not None:
df_c = df_compare.sort_values('close_time').copy()
df_c['_cum'] = df_c['net_profit'].cumsum()
fig.add_trace(go.Scatter(
x=df_c['close_time'], y=df_c['_cum'], mode='lines',
name=compare_label,
line=dict(color='#34C27A', width=2, dash='dash'),
fill='tozeroy', fillcolor='rgba(52,194,122,0.04)',
))
if show_strategy and 'strategy' in df_s.columns:
for i, strat in enumerate(sorted(df_s['strategy'].dropna().unique())):
@@ -302,53 +392,86 @@ def render():
)
st.plotly_chart(fig, use_container_width=True, key=f"eq_fig_{safe_key}")
# ── Drawdown panel ────────────────────────────────────────────────
st.markdown("**Drawdown**")
df_s['_cum2'] = df_s['net_profit'].cumsum()
df_s['_peak'] = df_s['_cum2'].cummax()
df_s['_dd'] = df_s['_cum2'] - df_s['_peak']
fig_dd = go.Figure()
fig_dd.add_trace(go.Scatter(
x=df_s['close_time'], y=df_s['_dd'],
mode='lines', fill='tozeroy',
line=dict(color='rgba(124,106,247,0.8)', width=1.5),
fillcolor='rgba(124,106,247,0.08)', name='DD Original',
))
if df_compare is not None:
df_c2 = df_compare.sort_values('close_time').copy()
df_c2['_cum2'] = df_c2['net_profit'].cumsum()
df_c2['_peak'] = df_c2['_cum2'].cummax()
df_c2['_dd'] = df_c2['_cum2'] - df_c2['_peak']
fig_dd.add_trace(go.Scatter(
x=df_c2['close_time'], y=df_c2['_dd'],
mode='lines', fill='tozeroy',
line=dict(color='rgba(220,80,80,0.8)', width=1.5),
fillcolor='rgba(220,80,80,0.08)', name=f'DD {compare_label}',
))
fig_dd.update_layout(
height=130,
plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)',
font=dict(family='sans-serif'),
xaxis=dict(gridcolor='rgba(128,128,128,0.15)', showgrid=True,
showticklabels=False),
yaxis=dict(gridcolor='rgba(128,128,128,0.15)', tickprefix='$',
showgrid=True),
margin=dict(l=60, r=20, t=8, b=4),
showlegend=False,
)
st.plotly_chart(fig_dd, use_container_width=True, key=f"eq_dd_{safe_key}")
# ── Daily P&L bars ────────────────────────────────────────────────
st.markdown("**Daily P&L**")
daily = (df_s.groupby(df_s['close_time'].dt.date)['net_profit']
.sum().reset_index())
daily.columns = ['date','pnl']
daily['color'] = daily['pnl'].apply(
lambda v: 'rgba(52,194,122,0.75)' if v >= 0 else 'rgba(220,80,80,0.75)')
fig_d = go.Figure(go.Bar(
x=daily['date'], y=daily['pnl'],
marker_color=daily['color'], name='Daily P&L',
))
fig_d.update_layout(
height=160,
plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)',
font=dict(family='sans-serif'),
xaxis=dict(gridcolor='rgba(128,128,128,0.15)', showgrid=False,
showticklabels=False),
yaxis=dict(gridcolor='rgba(128,128,128,0.15)', tickprefix='$',
showgrid=True, zeroline=True,
zerolinecolor='rgba(128,128,128,0.3)'),
margin=dict(l=60, r=20, t=8, b=20),
showlegend=False,
)
st.plotly_chart(fig_d, use_container_width=True, key=f"eq_daily_{safe_key}")
lambda v: 'rgba(52,194,122,0.85)' if v >= 0 else 'rgba(220,80,80,0.85)')
# ── Drawdown panel ────────────────────────────────────────────────
st.markdown("**Drawdown**")
df_s['_cum2'] = df_s['net_profit'].cumsum()
df_s['_peak'] = df_s['_cum2'].cummax()
df_s['_dd'] = df_s['_cum2'] - df_s['_peak']
fig_dd = go.Figure(go.Scatter(
x=df_s['close_time'], y=df_s['_dd'],
mode='lines', fill='tozeroy',
line=dict(color='rgba(220,80,80,0.6)', width=1),
fillcolor='rgba(220,80,80,0.12)', name='Drawdown',
fig_d = go.Figure()
fig_d.add_trace(go.Bar(
x=daily['date'], y=daily['pnl'],
marker_color=daily['color'],
name='Original',
offsetgroup=0,
))
fig_dd.update_layout(
height=120,
if df_compare is not None:
dc = df_compare.sort_values('close_time').copy()
daily_c = (dc.groupby(dc['close_time'].dt.date)['net_profit']
.sum().reset_index())
daily_c.columns = ['date','pnl']
daily_c['color'] = daily_c['pnl'].apply(
lambda v: 'rgba(124,106,247,0.45)' if v >= 0 else 'rgba(255,165,0,0.45)')
fig_d.add_trace(go.Bar(
x=daily_c['date'], y=daily_c['pnl'],
marker_color=daily_c['color'],
name=compare_label,
offsetgroup=1,
))
fig_d.update_layout(
height=160, barmode='group',
plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)',
font=dict(family='sans-serif'),
xaxis=dict(gridcolor='rgba(128,128,128,0.15)', showgrid=True),
yaxis=dict(gridcolor='rgba(128,128,128,0.15)', tickprefix='$',
showgrid=True),
margin=dict(l=60, r=20, t=8, b=40),
showlegend=False,
showgrid=True, zeroline=True,
zerolinecolor='rgba(128,128,128,0.4)'),
margin=dict(l=60, r=20, t=4, b=40),
legend=dict(bgcolor='rgba(0,0,0,0)', orientation='h',
yanchor='bottom', y=1.02),
showlegend=df_compare is not None,
)
st.plotly_chart(fig_dd, use_container_width=True, key=f"eq_dd_{safe_key}")
st.plotly_chart(fig_d, use_container_width=True, key=f"eq_daily_{safe_key}")
def render_dow_chart(df_plot):
dow_order = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
@@ -477,16 +600,55 @@ def render():
# ── Render mode ───────────────────────────────────────────────────────────
if mode == "Overall":
stats = calc_stats(df)
render_stats(stats, "Overall Statistics")
render_equity_curve(df)
stats = calc_stats(df)
stats_e = calc_stats(df_e) if df_e is not None else None
if view_sel == "Edited" and stats_e:
render_stats(stats_e, "Overall Statistics (Edited)")
elif view_sel == "Both" and stats_e:
render_stats(stats, "Overall Statistics", stats_compare=stats_e)
else:
render_stats(stats, "Overall Statistics")
if view_sel == "Both" and df_e is not None:
render_equity_curve(df, label="Equity Curve", df_compare=df_e,
compare_label="Edited")
elif view_sel == "Edited" and df_e is not None:
render_equity_curve(df_e, label="Equity Curve (Edited)")
else:
render_equity_curve(df)
_df_charts = df_e if (view_sel in ("Edited","Both") and df_e is not None) else df
col1, col2 = st.columns(2)
with col1:
render_dow_chart(df)
render_dow_chart(_df_charts)
with col2:
render_hour_chart(df)
render_hour_chart(_df_charts)
st.divider()
render_monthly_table(df, "Monthly Performance", key_prefix="mt_overall")
# ── Grouped trades summary (collapsible) ──────────────────────────
group_summary = st.session_state.get('ta_group_summary')
if group_summary and view_sel in ("Edited", "Both"):
import pandas as _pd
n_groups = len([r for r in group_summary if r['Group'] != '—'])
n_single = len([r for r in group_summary if r['Group'] == '—'])
with st.expander(
f"Position Summary — {len(group_summary)} positions "
f"({n_groups} grouped, {n_single} individual)", expanded=False):
st.caption("Grouped positions show merged entries. Individual trades show as single rows. Sorted by open time.")
gs_df = _pd.DataFrame(group_summary)
def _colour_pnl(val):
try:
v = float(val)
if v > 0: return 'background-color: rgba(52,194,122,0.15)'
if v < 0: return 'background-color: rgba(220,80,80,0.15)'
except: pass
return ''
st.dataframe(
gs_df.style.map(_colour_pnl, subset=['Net P&L ($)']),
use_container_width=True, hide_index=True
)
if view_sel == "Both" and df_e is not None:
render_monthly_table(df, "Monthly Performance (Original)", key_prefix="mt_overall_orig")
render_monthly_table(df_e,"Monthly Performance (Edited)", key_prefix="mt_overall_edit")
else:
render_monthly_table(_df_charts, "Monthly Performance", key_prefix="mt_overall")
elif mode == "By Strategy":
strats = sorted(df['strategy'].dropna().unique().tolist())
@@ -494,11 +656,12 @@ def render():
st.info("No strategies found")
else:
st.subheader("Strategy Comparison")
_df_s = df_e if (view_sel in ("Edited","Both") and df_e is not None) else df
rows = []
for s in strats:
sdf = df[df['strategy'] == s]
sdf = _df_s[_df_s['strategy'] == s] if s in _df_s['strategy'].values else df[df['strategy']==s]
stat = calc_stats(sdf)
rows.append({
row = {
'Strategy' : s,
'Trades' : stat['total_trades'],
'Net Profit' : stat['net_profit'],
@@ -509,24 +672,49 @@ def render():
'Max DD' : stat['max_drawdown'],
'Max Consec W' : stat['max_consec_wins'],
'Max Consec L' : stat['max_consec_losses'],
})
sdf_sum = __import__('pandas').DataFrame(rows).sort_values('Net Profit', ascending=False)
st.dataframe(
sdf_sum.style.map(colour_profit, subset=['Net Profit', 'Expectancy', 'Max DD']),
use_container_width=True, hide_index=True
)
}
if view_sel == "Both" and df_e is not None:
sdf_e = df_e[df_e['strategy'] == s] if s in df_e['strategy'].values else None
if sdf_e is not None and len(sdf_e):
stat_e = calc_stats(sdf_e)
def _arr(k, higher=''):
diff = stat_e[k] - stat[k]
if abs(diff) < 0.001: return ''
arrow = '▲' if diff > 0 else '▼'
color = 'green' if (diff > 0) == (k not in ('max_drawdown','max_consec_losses')) else 'red'
return f" {arrow}{abs(diff):.2f}"
row['Net Profit'] = f"{stat['net_profit']:.2f}{_arr('net_profit')}"
row['Win Rate %'] = f"{stat['win_rate']}{_arr('win_rate')}"
row['Profit Factor'] = f"{stat['profit_factor']}{_arr('profit_factor')}"
row['Expectancy'] = f"{stat['expectancy']:.2f}{_arr('expectancy')}"
row['Max DD'] = f"{stat['max_drawdown']:.2f}{_arr('max_drawdown')}"
rows.append(row)
import pandas as pd
sdf_sum = pd.DataFrame(rows).sort_values('Net Profit', ascending=False)
st.dataframe(sdf_sum, use_container_width=True, hide_index=True)
st.divider()
sel = st.selectbox("Select strategy for detail", strats)
if sel:
sdf = df[df['strategy'] == sel]
sdf = _df_s[_df_s['strategy'] == sel] if sel in _df_s['strategy'].values else df[df['strategy']==sel]
stat = calc_stats(sdf)
render_stats(stat, sel)
sdf_e_sel = df_e[df_e['strategy']==sel] if (df_e is not None and sel in df_e['strategy'].values) else None
stats_e_sel = calc_stats(sdf_e_sel) if sdf_e_sel is not None and len(sdf_e_sel) else None
if view_sel == "Both" and stats_e_sel:
render_stats(stat, sel, stats_compare=stats_e_sel)
elif view_sel == "Edited" and stats_e_sel:
render_stats(stats_e_sel, f"{sel} (Edited)")
else:
render_stats(stat, sel)
render_equity_curve(sdf, f"{sel} — Equity Curve")
col1, col2 = st.columns(2)
with col1: render_dow_chart(sdf)
with col2: render_hour_chart(sdf)
st.divider()
render_monthly_table(sdf, "Monthly Performance", key_prefix=f"mt_strat_{sel}")
if view_sel == "Both" and sdf_e_sel is not None and len(sdf_e_sel):
render_monthly_table(sdf, "Monthly Performance (Original)", key_prefix=f"mt_strat_orig_{sel}")
render_monthly_table(sdf_e_sel,"Monthly Performance (Edited)", key_prefix=f"mt_strat_edit_{sel}")
else:
render_monthly_table(sdf, "Monthly Performance", key_prefix=f"mt_strat_{sel}")
elif mode == "By Symbol":
syms = sorted(df['symbol'].dropna().unique().tolist())
@@ -544,34 +732,198 @@ def render():
'Expectancy' : stat['expectancy'],
'Max DD' : stat['max_drawdown'],
})
sdf_sum = __import__('pandas').DataFrame(rows).sort_values('Net Profit', ascending=False)
import pandas as pd
sdf_sum = pd.DataFrame(rows).sort_values('Net Profit', ascending=False)
st.dataframe(
sdf_sum.style.map(colour_profit, subset=['Net Profit', 'Expectancy', 'Max DD']),
use_container_width=True, hide_index=True
)
_df_sym = df_e if (view_sel in ("Edited","Both") and df_e is not None) else df
sel = st.selectbox("Select symbol for detail", syms)
if sel:
sdf = df[df['symbol'] == sel]
sdf = _df_sym[_df_sym['symbol'] == sel] if sel in _df_sym['symbol'].values else df[df['symbol']==sel]
stat = calc_stats(sdf)
render_stats(stat, sel)
sdf_e_sel = df_e[df_e['symbol']==sel] if (df_e is not None and sel in df_e['symbol'].values) else None
stats_e_sel = calc_stats(sdf_e_sel) if sdf_e_sel is not None and len(sdf_e_sel) else None
if view_sel == "Both" and stats_e_sel:
render_stats(stat, sel, stats_compare=stats_e_sel)
elif view_sel == "Edited" and stats_e_sel:
render_stats(stats_e_sel, f"{sel} (Edited)")
else:
render_stats(stat, sel)
render_equity_curve(sdf, f"{sel} — Equity Curve")
col1, col2 = st.columns(2)
with col1: render_dow_chart(sdf)
with col2: render_hour_chart(sdf)
st.divider()
render_monthly_table(sdf, "Monthly Performance", key_prefix=f"mt_sym_{sel}")
if view_sel == "Both" and sdf_e_sel is not None and len(sdf_e_sel):
render_monthly_table(sdf, "Monthly Performance (Original)", key_prefix=f"mt_sym_orig_{sel}")
render_monthly_table(sdf_e_sel,"Monthly Performance (Edited)", key_prefix=f"mt_sym_edit_{sel}")
else:
render_monthly_table(sdf, "Monthly Performance", key_prefix=f"mt_sym_{sel}")
elif mode == "By Day of Week":
render_dow_chart(df)
render_hour_chart(df)
_df_dow = df_e if (view_sel in ("Edited","Both") and df_e is not None) else df
render_dow_chart(_df_dow)
render_hour_chart(_df_dow)
# ── Raw trade log ─────────────────────────────────────────────────────────
st.divider()
with st.expander("Raw Trade Log"):
show_cols = ['open_time', 'close_time', 'symbol', 'type', 'strategy',
edit_cols = ['open_time', 'close_time', 'symbol', 'type', 'strategy',
'volume', 'open_price', 'close_price', 'sl', 'tp',
'commission', 'swap', 'profit', 'net_profit', 'duration_min']
show_cols = [c for c in show_cols if c in df.columns]
edit_cols = [c for c in edit_cols if c in st.session_state['ta_df'].columns]
# Show full dataset (not filtered) with trade index and Group column
df_edit = st.session_state['ta_df'][edit_cols].copy()
df_edit.insert(0, '#', range(1, len(df_edit) + 1))
# Preserve existing Group column if already edited
existing_edited = st.session_state.get('ta_df_edited')
if existing_edited is not None and 'Group' in existing_edited.columns:
df_edit.insert(1, 'Group', existing_edited['Group'].values[:len(df_edit)])
else:
df_edit.insert(1, 'Group', '')
st.caption(
"Edit any cell then click **Update**. "
"Enter the same label in **Group** for trades to merge into one position. "
"**Reset** restores the original upload."
)
bc1, bc2, bc3 = st.columns([1, 1, 6])
do_update = bc1.button("✅ Update", type="primary", key="ta_log_update")
do_reset = bc2.button("↩️ Reset", key="ta_log_reset")
edited = st.data_editor(
df_edit,
use_container_width=True,
hide_index=True,
height=400,
column_config={
'#': st.column_config.NumberColumn('#', disabled=True, width='small'),
'Group': st.column_config.TextColumn('Group', width='small',
help='Same label = merge into one trade on Update'),
'open_time': st.column_config.DatetimeColumn('open_time', format='YYYY-MM-DD HH:mm:ss'),
'close_time': st.column_config.DatetimeColumn('close_time', format='YYYY-MM-DD HH:mm:ss'),
'symbol': st.column_config.TextColumn('symbol'),
'type': st.column_config.SelectboxColumn('type', options=['buy','sell']),
'strategy': st.column_config.TextColumn('strategy'),
'volume': st.column_config.NumberColumn('volume', format='%.2f'),
'open_price': st.column_config.NumberColumn('open_price', format='%.5f'),
'close_price': st.column_config.NumberColumn('close_price', format='%.5f'),
'profit': st.column_config.NumberColumn('profit', format='%.2f'),
'net_profit': st.column_config.NumberColumn('net_profit', format='%.2f'),
},
key='ta_log_editor'
)
if do_update:
import pandas as pd
upd = edited.drop(columns=['#'])
for col in ['open_time','close_time']:
if col in upd.columns:
upd[col] = pd.to_datetime(upd[col], errors='coerce')
for col in ['profit','net_profit','volume','open_price','close_price',
'commission','swap','sl','tp','duration_min']:
if col in upd.columns:
upd[col] = pd.to_numeric(upd[col], errors='coerce')
# ── Merge grouped trades ──────────────────────────────────────
upd_with_groups = upd.copy() # preserve Group labels for summary
groups = upd['Group'].fillna('').str.strip()
ungrouped = upd[groups == ''].drop(columns=['Group'])
grouped_rows = []
for label, grp in upd[groups != ''].groupby(groups):
merged = {
'open_time': grp['open_time'].min(),
'close_time': grp['close_time'].max(),
'symbol': grp['symbol'].iloc[0],
'type': grp['type'].iloc[0],
'strategy': grp['strategy'].iloc[0],
'volume': grp['volume'].sum(),
'open_price': grp['open_price'].iloc[0],
'close_price': grp['close_price'].iloc[-1],
'profit': grp['profit'].sum() if 'profit' in grp else 0,
'net_profit': grp['net_profit'].sum(),
'commission': grp['commission'].sum() if 'commission' in grp else 0,
'swap': grp['swap'].sum() if 'swap' in grp else 0,
}
if 'sl' in grp: merged['sl'] = grp['sl'].iloc[0]
if 'tp' in grp: merged['tp'] = grp['tp'].iloc[0]
grouped_rows.append(merged)
if grouped_rows:
df_grouped = pd.DataFrame(grouped_rows)
upd = pd.concat([ungrouped, df_grouped], ignore_index=True)
upd = upd.sort_values('open_time').reset_index(drop=True)
else:
upd = ungrouped
upd['duration_min'] = ((upd['close_time'] - upd['open_time'])
.dt.total_seconds() / 60).round(1)
upd['win'] = upd['net_profit'] > 0
upd['day_of_week'] = upd['open_time'].dt.day_name()
upd['hour'] = upd['open_time'].dt.hour
# Preserve non-editable columns
orig = st.session_state['ta_df']
for col in orig.columns:
if col not in upd.columns:
upd[col] = orig[col].values[:len(upd)]
upd['comment'] = upd.get('comment', '')
upd['source'] = upd.get('source', 'manual')
st.session_state['ta_df_edited'] = upd
# Build full position summary — grouped and ungrouped trades
summary_rows = []
grp_labels = upd_with_groups['Group'].fillna('').str.strip()
# Grouped trades first
for label, grp in upd_with_groups[grp_labels != ''].groupby(grp_labels[grp_labels != '']):
net = grp['net_profit'].sum()
summary_rows.append({
'Group': label,
'Entries': len(grp),
'Symbol': grp['symbol'].iloc[0],
'Type': grp['type'].iloc[0],
'Open Time': grp['open_time'].min(),
'Close Time': grp['close_time'].max(),
'Total Volume': round(grp['volume'].sum(), 2),
'Net P&L ($)': round(net, 2),
'Win': '✅' if net > 0 else '❌',
})
# Individual (ungrouped) trades
for _, row in upd_with_groups[grp_labels == ''].iterrows():
net = row['net_profit']
summary_rows.append({
'Group': '—',
'Entries': 1,
'Symbol': row['symbol'],
'Type': row['type'],
'Open Time': row['open_time'],
'Close Time': row['close_time'],
'Total Volume': round(row['volume'], 2),
'Net P&L ($)': round(net, 2),
'Win': '✅' if net > 0 else '❌',
})
# Sort by open time
summary_rows.sort(key=lambda r: r['Open Time'] if r['Open Time'] is not None else pd.Timestamp.min)
st.session_state['ta_group_summary'] = summary_rows if summary_rows else None
n_merged = len(groups[groups != ''].unique())
st.success(
f"Saved — {len(upd)} trades "
f"({n_merged} group(s) merged). "
"Select 'Edited' or 'Both' to compare."
)
st.rerun()
if do_reset:
st.session_state['ta_df_edited'] = None
st.session_state['ta_group_summary'] = None
st.success("Edited version cleared.")
st.rerun()
def colour_net(val):
try:
@@ -582,13 +934,14 @@ def render():
pass
return ''
st.dataframe(
df[show_cols].style.map(colour_net, subset=['net_profit', 'profit']),
use_container_width=True, hide_index=True, height=400
)
st.divider()
# Download uses edited version if available, otherwise original
_dl_df = st.session_state['ta_df_edited'] if st.session_state.get('ta_df_edited') is not None else st.session_state['ta_df']
_dl_cols = [c for c in edit_cols if c in _dl_df.columns]
_dl_label = "Edited" if st.session_state.get('ta_df_edited') is not None else "Original"
st.download_button(
"⬇ Download filtered trades CSV",
data = df[show_cols].to_csv(index=False),
f"⬇ Download {_dl_label} trades CSV",
data = _dl_df[_dl_cols].to_csv(index=False),
file_name = f"mt5_trades_{date_from}_{date_to}.csv",
mime = 'text/csv'
)