unknown
423 lines
18 KiB
Python
423 lines
18 KiB
Python
"""
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pages/trade_compare.py
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======================
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Side-by-side comparison of two trade history files.
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Matches trades by symbol + type + open time within a tolerance window.
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Highlights slippage, profit variance, and timing differences.
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"""
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import streamlit as st
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import pandas as pd
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import plotly.graph_objects as go
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import sys, os
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from mt5_parser import detect_and_parse, calc_stats
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# ── Match trades ──────────────────────────────────────────────────────────────
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def match_trades(df_a, df_b, tolerance_hours):
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"""
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Match trades between two DataFrames.
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Match criteria: same symbol_base + same type + open_time within tolerance.
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Returns DataFrame of matched pairs with diff columns.
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"""
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tol = pd.Timedelta(hours=tolerance_hours)
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matched = []
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used_b = set()
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for i, a in df_a.iterrows():
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best_match = None
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best_delta = tol + pd.Timedelta(seconds=1)
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for j, b in df_b.iterrows():
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if j in used_b:
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continue
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if a['symbol_base'] != b['symbol_base']:
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continue
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if a['type'] != b['type']:
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continue
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delta = abs(a['open_time'] - b['open_time'])
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if delta <= tol and delta < best_delta:
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best_delta = delta
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best_match = (j, b)
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if best_match:
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j, b = best_match
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used_b.add(j)
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open_slip = round(float(b['open_price']) - float(a['open_price']), 5) if pd.notna(a['open_price']) and pd.notna(b['open_price']) else None
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close_slip = round(float(b['close_price']) - float(a['close_price']), 5) if pd.notna(a['close_price']) and pd.notna(b['close_price']) else None
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profit_var = round(float(b['net_profit']) - float(a['net_profit']), 2) if pd.notna(a['net_profit']) and pd.notna(b['net_profit']) else None
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time_diff = round((b['open_time'] - a['open_time']).total_seconds() / 60, 1)
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dur_diff = round(float(b.get('duration_min', 0) or 0) - float(a.get('duration_min', 0) or 0), 1)
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matched.append({
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# File A
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'A_open_time' : a['open_time'],
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'A_close_time' : a['close_time'],
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'A_symbol' : a['symbol'],
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'A_type' : a['type'],
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'A_volume' : a.get('volume'),
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'A_open_price' : a.get('open_price'),
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'A_close_price': a.get('close_price'),
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'A_profit' : a.get('net_profit'),
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'A_duration' : a.get('duration_min'),
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# File B
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'B_open_time' : b['open_time'],
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'B_close_time' : b['close_time'],
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'B_symbol' : b['symbol'],
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'B_type' : b['type'],
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'B_volume' : b.get('volume'),
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'B_open_price' : b.get('open_price'),
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'B_close_price': b.get('close_price'),
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'B_profit' : b.get('net_profit'),
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'B_duration' : b.get('duration_min'),
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# Differences
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'open_slippage' : open_slip,
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'close_slippage': close_slip,
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'profit_var' : profit_var,
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'time_diff_min' : time_diff,
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'duration_diff' : dur_diff,
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})
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return pd.DataFrame(matched)
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# ── Render ────────────────────────────────────────────────────────────────────
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def render():
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st.title("🔄 Trade Compare")
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st.markdown("""
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<div class="info-card">
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Compare two trade history files — backtest vs real account, or any two exports.
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Trades are matched by symbol, direction, and open time within a configurable
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tolerance window to account for gaps, slippage, and market open variations.
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</div>
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""", unsafe_allow_html=True)
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# ── Session state ─────────────────────────────────────────────────────────
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for k in ['tc_df_a', 'tc_df_b', 'tc_fmt_a', 'tc_fmt_b']:
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if k not in st.session_state:
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st.session_state[k] = None
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# ── File upload ───────────────────────────────────────────────────────────
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st.subheader("Load Files")
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col_a, col_b = st.columns(2)
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with col_a:
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st.markdown("**File A** — Reference (e.g. Backtest)")
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up_a = st.file_uploader("Upload File A", type=['html','htm','csv'], key='tc_up_a')
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if up_a:
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df_a, fmt_a = detect_and_parse(up_a.read(), up_a.name)
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if df_a is not None:
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st.session_state['tc_df_a'] = df_a
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st.session_state['tc_fmt_a'] = fmt_a
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st.success(f"✓ {len(df_a)} trades — {fmt_a}")
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else:
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st.error("Could not parse File A")
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if st.session_state['tc_df_a'] is not None:
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st.caption(f"Loaded: **{st.session_state['tc_fmt_a']}** · {len(st.session_state['tc_df_a'])} trades")
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with col_b:
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st.markdown("**File B** — Comparison (e.g. Real Account)")
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up_b = st.file_uploader("Upload File B", type=['html','htm','csv'], key='tc_up_b')
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if up_b:
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df_b, fmt_b = detect_and_parse(up_b.read(), up_b.name)
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if df_b is not None:
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st.session_state['tc_df_b'] = df_b
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st.session_state['tc_fmt_b'] = fmt_b
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st.success(f"✓ {len(df_b)} trades — {fmt_b}")
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else:
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st.error("Could not parse File B")
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if st.session_state['tc_df_b'] is not None:
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st.caption(f"Loaded: **{st.session_state['tc_fmt_b']}** · {len(st.session_state['tc_df_b'])} trades")
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df_a = st.session_state['tc_df_a']
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df_b = st.session_state['tc_df_b']
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if df_a is None or df_b is None:
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return
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# ── Filters ───────────────────────────────────────────────────────────────
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st.divider()
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st.subheader("Filters")
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fa1, fa2, fa3 = st.columns(3)
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fb1, fb2, fb3 = st.columns(3)
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with fa1:
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st.markdown("**File A filters**")
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with fb1:
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st.markdown("**File B filters**")
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col1, col2, col3, col4, col5, col6 = st.columns(6)
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with col1:
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a_date_min = df_a['open_time'].min().date()
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a_date_max = df_a['open_time'].max().date()
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a_from = st.date_input("A — From", value=a_date_min, min_value=a_date_min,
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max_value=a_date_max, key='tc_a_from')
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a_to = st.date_input("A — To", value=a_date_max, min_value=a_date_min,
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max_value=a_date_max, key='tc_a_to')
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with col2:
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a_syms = sorted(df_a['symbol'].dropna().unique().tolist())
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a_sel_sym = st.multiselect("A — Symbol", a_syms, key='tc_a_sym')
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with col3:
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a_strats = sorted(df_a['strategy'].dropna().unique().tolist())
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a_sel_strat = st.multiselect("A — Strategy", a_strats, key='tc_a_strat')
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a_sel_type = st.multiselect("A — Type", ['buy', 'sell'], key='tc_a_type')
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with col4:
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b_date_min = df_b['open_time'].min().date()
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b_date_max = df_b['open_time'].max().date()
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b_from = st.date_input("B — From", value=b_date_min, min_value=b_date_min,
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max_value=b_date_max, key='tc_b_from')
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b_to = st.date_input("B — To", value=b_date_max, min_value=b_date_min,
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max_value=b_date_max, key='tc_b_to')
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with col5:
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b_syms = sorted(df_b['symbol'].dropna().unique().tolist())
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b_sel_sym = st.multiselect("B — Symbol", b_syms, key='tc_b_sym')
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with col6:
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b_strats = sorted(df_b['strategy'].dropna().unique().tolist())
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b_sel_strat = st.multiselect("B — Strategy", b_strats, key='tc_b_strat')
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b_sel_type = st.multiselect("B — Type", ['buy', 'sell'], key='tc_b_type')
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# ── Matching tolerance ────────────────────────────────────────────────────
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st.divider()
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col_tol, col_run = st.columns([3, 1])
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with col_tol:
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tolerance = st.slider(
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"Match tolerance (hours) — max time difference between A and B open times",
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min_value=1, max_value=24, value=4, step=1,
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help="Trades within this window are considered the same setup. "
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"Increase for daily charts, decrease for intraday."
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)
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with col_run:
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st.markdown("<br>", unsafe_allow_html=True)
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run = st.button("🔍 Match Trades", type="primary", use_container_width=True)
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if not run and 'tc_matched' not in st.session_state:
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return
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# Apply filters
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fa = df_a.copy()
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fa = fa[(fa['open_time'].dt.date >= a_from) & (fa['open_time'].dt.date <= a_to)]
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if a_sel_sym: fa = fa[fa['symbol'].isin(a_sel_sym)]
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if a_sel_strat: fa = fa[fa['strategy'].isin(a_sel_strat)]
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if a_sel_type: fa = fa[fa['type'].isin(a_sel_type)]
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fb = df_b.copy()
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fb = fb[(fb['open_time'].dt.date >= b_from) & (fb['open_time'].dt.date <= b_to)]
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if b_sel_sym: fb = fb[fb['symbol'].isin(b_sel_sym)]
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if b_sel_strat: fb = fb[fb['strategy'].isin(b_sel_strat)]
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if b_sel_type: fb = fb[fb['type'].isin(b_sel_type)]
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if run:
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with st.spinner("Matching trades..."):
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matched = match_trades(fa, fb, tolerance)
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st.session_state['tc_matched'] = matched
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st.session_state['tc_fa_len'] = len(fa)
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st.session_state['tc_fb_len'] = len(fb)
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matched = st.session_state.get('tc_matched', pd.DataFrame())
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fa_len = st.session_state.get('tc_fa_len', len(fa))
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fb_len = st.session_state.get('tc_fb_len', len(fb))
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if matched is None or len(matched) == 0:
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st.warning("No matching trades found — try increasing the tolerance window or adjusting filters.")
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return
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# ── Summary stats ─────────────────────────────────────────────────────────
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st.divider()
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st.subheader("Match Summary")
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m1, m2, m3, m4, m5 = st.columns(5)
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m1.metric("File A Trades", fa_len)
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m2.metric("File B Trades", fb_len)
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m3.metric("Matched Pairs", len(matched))
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m4.metric("Unmatched A", fa_len - len(matched))
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m5.metric("Unmatched B", fb_len - len(matched))
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st.divider()
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# ── Aggregate comparison ───────────────────────────────────────────────────
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st.subheader("Aggregate Comparison")
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ac1, ac2 = st.columns(2)
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with ac1:
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st.markdown("**File A (Reference)**")
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a_net = matched['A_profit'].sum()
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a_wr = (matched['A_profit'] > 0).mean() * 100
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a_avg = matched['A_profit'].mean()
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a_dur = matched['A_duration'].mean() if 'A_duration' in matched else None
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st.metric("Net Profit", f"${a_net:,.2f}")
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st.metric("Win Rate", f"{a_wr:.1f}%")
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st.metric("Avg Profit", f"${a_avg:,.2f}")
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if a_dur:
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st.metric("Avg Duration", f"{a_dur:.0f}m")
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with ac2:
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st.markdown("**File B (Comparison)**")
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b_net = matched['B_profit'].sum()
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b_wr = (matched['B_profit'] > 0).mean() * 100
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b_avg = matched['B_profit'].mean()
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b_dur = matched['B_duration'].mean() if 'B_duration' in matched else None
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delta_net = b_net - a_net
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st.metric("Net Profit", f"${b_net:,.2f}",
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delta=f"{delta_net:+.2f}", delta_color="normal")
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st.metric("Win Rate", f"{b_wr:.1f}%",
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delta=f"{b_wr - a_wr:+.1f}%", delta_color="normal")
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st.metric("Avg Profit", f"${b_avg:,.2f}",
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delta=f"{b_avg - a_avg:+.2f}", delta_color="normal")
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if b_dur and a_dur:
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st.metric("Avg Duration", f"{b_dur:.0f}m",
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delta=f"{b_dur - a_dur:+.0f}m", delta_color="off")
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# ── Slippage summary ───────────────────────────────────────────────────────
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st.divider()
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st.subheader("Slippage & Variance Summary")
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sc1, sc2, sc3, sc4 = st.columns(4)
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avg_open_slip = matched['open_slippage'].mean()
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avg_close_slip = matched['close_slip'].mean() if 'close_slip' in matched else matched['close_slippage'].mean()
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avg_profit_var = matched['profit_var'].mean()
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avg_time_diff = matched['time_diff_min'].mean()
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sc1.metric("Avg Entry Slippage", f"{avg_open_slip:+.5f}" if pd.notna(avg_open_slip) else "N/A",
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help="B open price minus A open price. Positive = B filled higher.")
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sc2.metric("Avg Exit Slippage", f"{avg_close_slip:+.5f}" if pd.notna(avg_close_slip) else "N/A",
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help="B close price minus A close price.")
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sc3.metric("Avg Profit Variance", f"${avg_profit_var:+.2f}" if pd.notna(avg_profit_var) else "N/A",
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help="B net profit minus A net profit per trade.")
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sc4.metric("Avg Time Difference", f"{avg_time_diff:+.0f}m" if pd.notna(avg_time_diff) else "N/A",
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help="B open time minus A open time in minutes.")
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# ── Equity curve overlay ───────────────────────────────────────────────────
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st.divider()
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st.subheader("Equity Curve Overlay")
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m_sorted = matched.sort_values('A_open_time')
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fig = go.Figure()
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fig.add_trace(go.Scatter(
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x=m_sorted['A_open_time'],
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y=m_sorted['A_profit'].cumsum(),
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mode='lines', name='File A',
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line=dict(color='#7c6af7', width=2),
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fill='tozeroy', fillcolor='rgba(124,106,247,0.05)'
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))
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fig.add_trace(go.Scatter(
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x=m_sorted['B_open_time'],
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y=m_sorted['B_profit'].cumsum(),
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mode='lines', name='File B',
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line=dict(color='#2dc653', width=2),
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fill='tozeroy', fillcolor='rgba(45,198,83,0.05)'
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))
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fig.update_layout(
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height=320,
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plot_bgcolor='rgba(10,10,15,1)',
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paper_bgcolor='rgba(10,10,15,1)',
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font=dict(color='#aaa', family='JetBrains Mono'),
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xaxis=dict(gridcolor='rgba(255,255,255,0.04)'),
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yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
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legend=dict(bgcolor='rgba(0,0,0,0.3)'),
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margin=dict(l=60, r=20, t=20, b=40)
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)
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st.plotly_chart(fig, use_container_width=True)
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# ── Profit variance scatter ────────────────────────────────────────────────
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st.subheader("Profit Variance per Trade")
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fig2 = go.Figure()
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colours = matched['profit_var'].apply(
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lambda v: 'rgba(45,198,83,0.7)' if v >= 0 else 'rgba(230,57,70,0.7)'
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)
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fig2.add_trace(go.Bar(
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x=list(range(len(matched))),
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y=matched['profit_var'],
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marker_color=colours,
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name='Profit Variance (B - A)'
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))
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fig2.update_layout(
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height=250,
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plot_bgcolor='rgba(10,10,15,1)',
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paper_bgcolor='rgba(10,10,15,1)',
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font=dict(color='#aaa'),
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xaxis=dict(gridcolor='rgba(255,255,255,0.04)', title='Trade #'),
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yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
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margin=dict(l=60, r=20, t=20, b=40)
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)
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st.plotly_chart(fig2, use_container_width=True)
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# ── Matched trade table ────────────────────────────────────────────────────
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st.divider()
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st.subheader("Matched Trade Detail")
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def colour_diff(val):
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try:
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v = float(str(val).replace('+', ''))
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if v > 0: return 'color: #2dc653; font-weight: 600'
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if v < 0: return 'color: #e63946; font-weight: 600'
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except:
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pass
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return 'color: #666'
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def colour_profit_cell(val):
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try:
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v = float(str(val).replace(',', ''))
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if v > 0: return 'background-color: rgba(0,180,0,0.10)'
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if v < 0: return 'background-color: rgba(180,0,0,0.10)'
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except:
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pass
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return ''
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display = matched[[
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'A_open_time', 'A_symbol', 'A_type',
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'A_open_price', 'A_close_price', 'A_profit', 'A_duration',
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'B_open_time',
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'B_open_price', 'B_close_price', 'B_profit', 'B_duration',
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'open_slippage', 'close_slippage', 'profit_var', 'time_diff_min'
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]].copy()
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display.columns = [
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'A Open Time', 'Symbol', 'Type',
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'A Entry', 'A Exit', 'A Profit', 'A Dur(m)',
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'B Open Time',
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'B Entry', 'B Exit', 'B Profit', 'B Dur(m)',
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'Entry Slip', 'Exit Slip', 'Profit Var', 'Time Diff(m)'
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]
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# Format numeric columns
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for col in ['A Entry', 'A Exit', 'B Entry', 'B Exit']:
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if col in display.columns:
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display[col] = display[col].apply(
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lambda x: f"{x:.5f}" if pd.notna(x) else '')
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for col in ['A Profit', 'B Profit', 'Profit Var']:
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display[col] = display[col].apply(
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lambda x: f"{x:+.2f}" if pd.notna(x) else '')
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for col in ['Entry Slip', 'Exit Slip']:
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display[col] = display[col].apply(
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lambda x: f"{x:+.5f}" if pd.notna(x) else '')
|
|
|
|
st.dataframe(
|
|
display.style
|
|
.map(colour_diff, subset=['Entry Slip', 'Exit Slip', 'Profit Var', 'Time Diff(m)'])
|
|
.map(colour_profit_cell, subset=['A Profit', 'B Profit']),
|
|
use_container_width=True, hide_index=True, height=500
|
|
)
|
|
|
|
# ── Export ────────────────────────────────────────────────────────────────
|
|
st.download_button(
|
|
"⬇ Download matched trades CSV",
|
|
data = display.to_csv(index=False),
|
|
file_name = "trade_comparison.csv",
|
|
mime = 'text/csv'
|
|
)
|