unknown
fa5ceca982
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
947 lines
47 KiB
Python
947 lines
47 KiB
Python
"""
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pages/trade_analysis.py
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=======================
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MT5 Trade Analysis page — migrated from main dashboard.
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"""
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import streamlit as st
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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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def _normalise_ic(df):
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"""Map IC Markets DataFrame columns to the schema expected by calc_stats."""
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import pandas as pd
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out = df.copy()
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# calc_stats / render helpers need: open_time, close_time, symbol, type,
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# strategy, net_profit, win, volume, open_price, close_price,
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# commission, swap, profit, duration_min, day_of_week, hour
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if "symbol_base" in out.columns and "strategy" not in out.columns:
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out["strategy"] = out["symbol_base"]
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if "net_profit" in out.columns and "profit" not in out.columns:
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out["profit"] = out["net_profit"]
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if "commission" not in out.columns:
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out["commission"] = 0.0
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if "swap" not in out.columns:
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out["swap"] = 0.0
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if "sl" not in out.columns:
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out["sl"] = None
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if "tp" not in out.columns:
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out["tp"] = None
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# Ensure win column
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if "win" not in out.columns and "net_profit" in out.columns:
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out["win"] = out["net_profit"] > 0
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# Ensure day_of_week and hour
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if "open_time" in out.columns:
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out["open_time"] = pd.to_datetime(out["open_time"], errors="coerce")
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if "day_of_week" not in out.columns:
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out["day_of_week"] = out["open_time"].dt.day_name()
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if "hour" not in out.columns:
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out["hour"] = out["open_time"].dt.hour
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if "close_time" in out.columns:
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out["close_time"] = pd.to_datetime(out["close_time"], errors="coerce")
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if "duration_min" not in out.columns and "open_time" in out.columns and "close_time" in out.columns:
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out["duration_min"] = ((out["close_time"] - out["open_time"])
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.dt.total_seconds() / 60).round(1)
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return out
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def render():
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st.title("📊 Trade Analysis")
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# ── Session state ─────────────────────────────────────────────────────────
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for _k, _v in {
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'ta_df': None, 'ta_format': None,
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'ta_accounts': [], 'ta_ic_bytes': None,
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'ta_df_original': None,
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'ta_df_edited': None,
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'ta_group_summary': None,
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}.items():
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if _k not in st.session_state:
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st.session_state[_k] = _v
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# ── Source selector ──────────────────────────────────────────────────────
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src_col1, src_col2 = st.columns([4, 1])
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with src_col1:
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source = st.radio(
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"File source",
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["MT5 / Quant Analyzer", "IC Markets XLSX"],
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horizontal=True, key='ta_source',
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)
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with src_col2:
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st.markdown("<br>", unsafe_allow_html=True)
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if st.button("🗑 Clear", key='ta_clear'):
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st.session_state['ta_df'] = None
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st.session_state['ta_format'] = None
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st.session_state['ta_accounts'] = []
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st.rerun()
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# ── File upload ───────────────────────────────────────────────────────────
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if source == "MT5 / Quant Analyzer":
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uploaded = st.file_uploader(
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"Upload MT5 Report (HTM/HTML) or Quant Analyzer CSV",
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type=None, key='ta_upload',
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)
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if uploaded and uploaded.name.lower().endswith(('.htm','.html','.csv')):
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df, fmt = detect_and_parse(uploaded.read(), uploaded.name)
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if df is not None:
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st.session_state['ta_df'] = df
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st.session_state['ta_df_original'] = df.copy()
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st.session_state['ta_format'] = fmt
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st.session_state['ta_accounts'] = []
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st.success(f"✓ Loaded {len(df)} trades — {fmt}")
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else:
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st.error("Could not parse report — check file format")
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elif uploaded:
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st.warning("Please upload a .htm, .html, or .csv file.")
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else: # IC Markets XLSX
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uploaded = st.file_uploader(
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"Upload IC Markets Position History (.xlsx)",
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type=None, key='ta_upload',
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)
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if uploaded and uploaded.name.lower().endswith(('.xlsx','.xls')):
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try:
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from icmarkets_parser import get_icmarkets_accounts, parse_icmarkets_xlsx
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except ImportError as e:
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st.error(f"icmarkets_parser.py not found — ensure it is in the MT5Tools folder. ({e})")
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uploaded = None
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if uploaded:
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try:
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file_bytes = uploaded.read()
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accounts = get_icmarkets_accounts(file_bytes)
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if not accounts:
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st.error("No accounts found — check this is an IC Markets Position History export.")
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else:
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st.session_state['ta_ic_bytes'] = file_bytes
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st.session_state['ta_accounts'] = accounts
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st.session_state['ta_format'] = "IC Markets XLSX"
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df_ic = parse_icmarkets_xlsx(file_bytes, account=accounts[0])
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df_ic = _normalise_ic(df_ic)
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st.session_state['ta_df'] = df_ic
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st.session_state['ta_df_original'] = df_ic.copy()
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st.success(f"✓ Loaded {len(df_ic)} trades — {len(accounts)} account(s) found")
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except Exception as e:
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st.error(f"Error parsing file: {e}")
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import traceback; st.code(traceback.format_exc())
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elif uploaded:
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st.warning("Please upload an .xlsx file.")
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# IC Markets account selector (shown after upload)
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if (st.session_state.get('ta_accounts') and
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st.session_state.get('ta_source', source) == "IC Markets XLSX"):
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accounts = st.session_state['ta_accounts']
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ac_opts = ["All accounts"] + accounts
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sel_ac = st.selectbox("Account", ac_opts, key='ta_ic_account')
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acct = None if sel_ac == "All accounts" else sel_ac
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if st.session_state.get('ta_ic_bytes'):
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from icmarkets_parser import parse_icmarkets_xlsx
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df_ic = parse_icmarkets_xlsx(st.session_state['ta_ic_bytes'], account=acct)
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df_ic = _normalise_ic(df_ic)
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st.session_state['ta_df'] = df_ic
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df_all = st.session_state['ta_df']
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df_edited = st.session_state.get('ta_df_edited')
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fmt = st.session_state['ta_format']
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if df_all is None or len(df_all) == 0:
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st.markdown("""
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<div class="info-card">
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Upload an MT5 account history report (.htm/.html), MT5 backtest report,
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or a Quant Analyzer CSV export to begin analysis.
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</div>
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""", unsafe_allow_html=True)
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return
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if fmt:
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st.caption(f"Format detected: **{fmt}** · {len(df_all)} total trades")
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# ── View selector ─────────────────────────────────────────────────────────
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has_edited = st.session_state.get('ta_df_edited') is not None
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if has_edited:
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view_opts = ["Original", "Edited", "Both"]
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view_sel = st.radio("View", view_opts, horizontal=True, key='ta_view_sel')
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else:
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view_sel = "Original"
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st.session_state['ta_view_sel'] = "Original"
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# ── Filters ───────────────────────────────────────────────────────────────
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st.divider()
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if 'ta_deposit' not in st.session_state:
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st.session_state['ta_deposit'] = 10000.0
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fc1, fc2, fc3, fc4 = st.columns(4)
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with fc1:
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valid_times = df_all['open_time'].dropna()
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date_min = valid_times.min().date()
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date_max = valid_times.max().date()
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date_from = st.date_input("From", value=date_min, min_value=date_min,
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max_value=date_max, key='ta_from')
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date_to = st.date_input("To", value=date_max, min_value=date_min,
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max_value=date_max, key='ta_to')
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with fc2:
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symbols = sorted(df_all['symbol'].dropna().unique().tolist())
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sel_symbol = st.multiselect("Symbol", symbols, key='ta_sym')
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with fc3:
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strategies = sorted(df_all['strategy'].dropna().unique().tolist())
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sel_strategy = st.multiselect("Strategy / EA", strategies, key='ta_strat')
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with fc4:
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days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
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sel_days = st.multiselect("Day of week", days, key='ta_days')
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sel_type = st.multiselect("Type", ['buy', 'sell'], key='ta_type')
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trade_nums = [str(i) for i in range(1, len(df_all)+1)]
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sel_trades = st.multiselect("Trade #", trade_nums, key='ta_idx_sel',
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placeholder="All trades (filter by #)")
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# Apply filters
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def _apply_filters(src_df):
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d = src_df.copy()
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d = d[(d['open_time'].dt.date >= date_from) &
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(d['open_time'].dt.date <= date_to)]
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if sel_symbol: d = d[d['symbol'].isin(sel_symbol)]
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if sel_strategy: d = d[d['strategy'].isin(sel_strategy)]
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if sel_days: d = d[d['day_of_week'].isin(sel_days)]
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if sel_type: d = d[d['type'].isin(sel_type)]
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d = d.reset_index(drop=True)
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if sel_trades:
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sel_idx = [int(t)-1 for t in sel_trades if int(t)-1 < len(d)]
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d = d.iloc[sel_idx].reset_index(drop=True)
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return d
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df = _apply_filters(df_all)
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# Also prepare edited df if available
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df_e = _apply_filters(df_edited) if df_edited is not None else None
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st.caption(f"Showing **{len(df)}** trades after filters")
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# ── Analysis mode ─────────────────────────────────────────────────────────
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mode = st.radio(
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"Analysis mode",
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["Overall", "By Strategy", "By Symbol", "By Day of Week"],
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horizontal=True, key='ta_mode'
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)
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st.divider()
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# ── Helpers ───────────────────────────────────────────────────────────────
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def render_stats(stats, label="", stats_compare=None):
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if label:
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st.markdown(f"**{label}**")
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def _delta(key, fmt='$', higher_is_better=True):
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"""Return delta string for st.metric when compare stats available."""
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if stats_compare is None or key not in stats_compare:
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return None
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diff = stats_compare[key] - stats[key]
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if diff == 0:
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return None
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if fmt == '$':
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return f"${diff:+,.2f}"
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elif fmt == '%':
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return f"{diff:+.1f}%"
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elif fmt == 'x':
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return f"{diff:+.2f}"
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else:
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return f"{diff:+g}"
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def _inv_delta(key, fmt='$'):
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"""Delta where lower is better (e.g. drawdown, losses)."""
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if stats_compare is None or key not in stats_compare:
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return None
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diff = stats_compare[key] - stats[key]
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if diff == 0:
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return None
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if fmt == '$':
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return f"${diff:+,.2f}"
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elif fmt == '%':
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return f"{diff:+.1f}%"
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else:
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return f"{diff:+g}"
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c1, c2, c3, c4, c5 = st.columns(5)
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c1.metric("Net Profit", f"${stats['net_profit']:,.2f}",
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delta=_delta('net_profit','$'))
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c2.metric("Win Rate", f"{stats['win_rate']}%",
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delta=_delta('win_rate','%'))
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c3.metric("Profit Factor", f"{stats['profit_factor']}",
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delta=_delta('profit_factor','x'))
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c4.metric("R:R Ratio", f"{stats['rr_ratio']}",
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delta=_delta('rr_ratio','x'))
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c5.metric("Expectancy", f"${stats['expectancy']:,.2f}",
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delta=_delta('expectancy','$'))
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c1, c2, c3, c4, c5 = st.columns(5)
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c1.metric("Total Trades", stats['total_trades'],
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delta=_delta('total_trades',''))
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c2.metric("Avg Win", f"${stats['avg_win']:,.2f}",
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delta=_delta('avg_win','$'))
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c3.metric("Avg Loss", f"${stats['avg_loss']:,.2f}",
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delta=_inv_delta('avg_loss','$'), delta_color="inverse")
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c4.metric("Max DD", f"${stats['max_drawdown']:,.2f}",
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delta=_inv_delta('max_drawdown','$'), delta_color="inverse")
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c5.metric("Best Trade", f"${stats['best_trade']:,.2f}",
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delta=_delta('best_trade','$'))
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c1, c2, c3, c4, c5 = st.columns(5)
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c1.metric("Max Consec Wins", stats['max_consec_wins'],
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delta=_delta('max_consec_wins',''))
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c2.metric("Max Consec Losses", stats['max_consec_losses'],
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delta=_inv_delta('max_consec_losses',''), delta_color="inverse")
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c3.metric("Avg Win Dur", f"{stats['avg_win_duration']}m",
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delta=_delta('avg_win_duration',''))
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c4.metric("Avg Loss Dur", f"{stats['avg_loss_duration']}m",
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delta=_inv_delta('avg_loss_duration',''), delta_color="inverse")
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c5.metric("Worst Trade", f"${stats['worst_trade']:,.2f}",
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delta=_inv_delta('worst_trade','$'), delta_color="inverse")
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c1, c2, c3, c4 = st.columns(4)
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c1.metric("Long Trades", stats['long_trades'],
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delta=_delta('long_trades',''))
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c2.metric("Long Win Rate", f"{stats['long_win_rate']}%",
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delta=_delta('long_win_rate','%'))
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c3.metric("Short Trades", stats['short_trades'],
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delta=_delta('short_trades',''))
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c4.metric("Short Win Rate",f"{stats['short_win_rate']}%",
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delta=_delta('short_win_rate','%'))
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c1, c2, c3, c4 = st.columns(4)
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c1.metric("Trading Days", stats.get('trading_days', 0),
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delta=_delta('trading_days',''))
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c2.metric("Trades / Day", f"{stats.get('trades_per_day', 0)}",
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delta=_delta('trades_per_day','x'))
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|
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def render_equity_curve(df_plot, label="Equity Curve", df_compare=None, compare_label="Edited"):
|
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import pandas as pd
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df_s = df_plot.sort_values('close_time').copy()
|
|
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COLORS = ['#7c6af7','#34C27A','#F5A623','#E05555','#4C8EF5',
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'#A78BFA','#22D3EE','#FB923C','#F472B6','#86EFAC']
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safe_key = label.replace(" ","_").replace("/","_").replace("—","").strip("_")
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ov = st.columns(4)
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show_account = ov[0].checkbox("Account total", value=True, key=f"eq_acc_{safe_key}")
|
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show_strategy = ov[1].checkbox("By Strategy", value=False, key=f"eq_str_{safe_key}")
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show_symbol = ov[2].checkbox("By Symbol", value=False, key=f"eq_sym_{safe_key}")
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show_dow = ov[3].checkbox("By Day of Week", value=False, key=f"eq_dow_{safe_key}")
|
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|
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fig = go.Figure()
|
|
|
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if show_account:
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df_s['_cum'] = df_s['net_profit'].cumsum()
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fig.add_trace(go.Scatter(
|
|
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)',
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|
))
|
|
# 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())):
|
|
sub = df_s[df_s['strategy']==strat].copy()
|
|
sub['_cum'] = sub['net_profit'].cumsum()
|
|
fig.add_trace(go.Scatter(
|
|
x=sub['close_time'], y=sub['_cum'], mode='lines', name=strat,
|
|
line=dict(color=COLORS[(i+1)%len(COLORS)], width=1.5, dash='dot'),
|
|
))
|
|
|
|
if show_symbol and 'symbol' in df_s.columns:
|
|
for i, sym in enumerate(sorted(df_s['symbol'].dropna().unique())):
|
|
sub = df_s[df_s['symbol']==sym].copy()
|
|
sub['_cum'] = sub['net_profit'].cumsum()
|
|
fig.add_trace(go.Scatter(
|
|
x=sub['close_time'], y=sub['_cum'], mode='lines', name=sym,
|
|
line=dict(color=COLORS[(i+2)%len(COLORS)], width=1.5, dash='dash'),
|
|
))
|
|
|
|
if show_dow and 'day_of_week' in df_s.columns:
|
|
for i, dow in enumerate(['Monday','Tuesday','Wednesday','Thursday','Friday']):
|
|
sub = df_s[df_s['day_of_week']==dow].copy()
|
|
if sub.empty: continue
|
|
sub['_cum'] = sub['net_profit'].cumsum()
|
|
fig.add_trace(go.Scatter(
|
|
x=sub['close_time'], y=sub['_cum'], mode='lines', name=dow,
|
|
line=dict(color=COLORS[(i+3)%len(COLORS)], width=1.5),
|
|
))
|
|
|
|
fig.update_layout(
|
|
title=label, height=360,
|
|
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='$', showgrid=True),
|
|
margin=dict(l=60, r=20, t=40, b=40),
|
|
legend=dict(bgcolor='rgba(0,0,0,0)', borderwidth=0),
|
|
hovermode='x unified',
|
|
)
|
|
st.plotly_chart(fig, use_container_width=True, key=f"eq_fig_{safe_key}")
|
|
|
|
# ── Drawdown panel ────────────────────────────────────────────────
|
|
st.markdown("**Drawdown**")
|
|
df_s['_cum2'] = df_s['net_profit'].cumsum()
|
|
df_s['_peak'] = df_s['_cum2'].cummax()
|
|
df_s['_dd'] = df_s['_cum2'] - df_s['_peak']
|
|
fig_dd = go.Figure()
|
|
fig_dd.add_trace(go.Scatter(
|
|
x=df_s['close_time'], y=df_s['_dd'],
|
|
mode='lines', fill='tozeroy',
|
|
line=dict(color='rgba(124,106,247,0.8)', width=1.5),
|
|
fillcolor='rgba(124,106,247,0.08)', name='DD Original',
|
|
))
|
|
if df_compare is not None:
|
|
df_c2 = df_compare.sort_values('close_time').copy()
|
|
df_c2['_cum2'] = df_c2['net_profit'].cumsum()
|
|
df_c2['_peak'] = df_c2['_cum2'].cummax()
|
|
df_c2['_dd'] = df_c2['_cum2'] - df_c2['_peak']
|
|
fig_dd.add_trace(go.Scatter(
|
|
x=df_c2['close_time'], y=df_c2['_dd'],
|
|
mode='lines', fill='tozeroy',
|
|
line=dict(color='rgba(220,80,80,0.8)', width=1.5),
|
|
fillcolor='rgba(220,80,80,0.08)', name=f'DD {compare_label}',
|
|
))
|
|
fig_dd.update_layout(
|
|
height=130,
|
|
plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)',
|
|
font=dict(family='sans-serif'),
|
|
xaxis=dict(gridcolor='rgba(128,128,128,0.15)', showgrid=True,
|
|
showticklabels=False),
|
|
yaxis=dict(gridcolor='rgba(128,128,128,0.15)', tickprefix='$',
|
|
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(52,194,122,0.85)' if v >= 0 else 'rgba(220,80,80,0.85)')
|
|
|
|
fig_d = go.Figure()
|
|
fig_d.add_trace(go.Bar(
|
|
x=daily['date'], y=daily['pnl'],
|
|
marker_color=daily['color'],
|
|
name='Original',
|
|
offsetgroup=0,
|
|
))
|
|
|
|
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='$',
|
|
showgrid=True, zeroline=True,
|
|
zerolinecolor='rgba(128,128,128,0.4)'),
|
|
margin=dict(l=60, r=20, t=4, b=40),
|
|
legend=dict(bgcolor='rgba(0,0,0,0)', orientation='h',
|
|
yanchor='bottom', y=1.02),
|
|
showlegend=df_compare is not None,
|
|
)
|
|
st.plotly_chart(fig_d, use_container_width=True, key=f"eq_daily_{safe_key}")
|
|
|
|
def render_dow_chart(df_plot):
|
|
dow_order = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
|
|
dow = df_plot.groupby('day_of_week').agg(
|
|
trades = ('net_profit', 'count'),
|
|
net_profit = ('net_profit', 'sum'),
|
|
win_rate = ('win', lambda x: round(x.mean()*100, 1))
|
|
).reindex([d for d in dow_order if d in df_plot['day_of_week'].unique()])
|
|
|
|
wins_dow = df_plot[df_plot['win']].groupby('day_of_week')['net_profit'].sum().reindex(dow.index, fill_value=0)
|
|
losses_dow = df_plot[~df_plot['win']].groupby('day_of_week')['net_profit'].sum().reindex(dow.index, fill_value=0)
|
|
|
|
fig = go.Figure()
|
|
fig.add_trace(go.Bar(x=dow.index, y=wins_dow, name='Profit', marker_color='rgba(45,198,83,0.8)'))
|
|
fig.add_trace(go.Bar(x=dow.index, y=losses_dow, name='Loss', marker_color='rgba(230,57,70,0.8)'))
|
|
fig.update_layout(
|
|
title='P&L by Day of Week', height=280, barmode='relative',
|
|
plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)',
|
|
font=dict(family='sans-serif'), margin=dict(l=60, r=20, t=40, b=40),
|
|
xaxis=dict(gridcolor='rgba(128,128,128,0.15)'),
|
|
yaxis=dict(gridcolor='rgba(128,128,128,0.15)', tickprefix='$'),
|
|
legend=dict(bgcolor='rgba(0,0,0,0)')
|
|
)
|
|
st.plotly_chart(fig, use_container_width=True)
|
|
|
|
dt = dow.reset_index()
|
|
dt.columns = ['Day', 'Trades', 'Net Profit', 'Win Rate %']
|
|
dt['Net Profit'] = dt['Net Profit'].round(2)
|
|
st.dataframe(dt, use_container_width=True, hide_index=True)
|
|
|
|
def render_hour_chart(df_plot):
|
|
hourly = df_plot.groupby('hour').agg(
|
|
trades = ('net_profit', 'count'),
|
|
net_profit = ('net_profit', 'sum'),
|
|
)
|
|
wins_h = df_plot[df_plot['win']].groupby('hour')['net_profit'].sum().reindex(hourly.index, fill_value=0)
|
|
losses_h = df_plot[~df_plot['win']].groupby('hour')['net_profit'].sum().reindex(hourly.index, fill_value=0)
|
|
|
|
fig = go.Figure()
|
|
fig.add_trace(go.Bar(x=wins_h.index, y=wins_h, name='Profit', marker_color='rgba(45,198,83,0.8)'))
|
|
fig.add_trace(go.Bar(x=losses_h.index, y=losses_h, name='Loss', marker_color='rgba(230,57,70,0.8)'))
|
|
fig.update_layout(
|
|
title='P&L by Hour of Day', height=280, barmode='relative',
|
|
plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)',
|
|
font=dict(family='sans-serif'), margin=dict(l=60, r=20, t=40, b=40),
|
|
xaxis=dict(gridcolor='rgba(128,128,128,0.15)', title='Hour (UTC)'),
|
|
yaxis=dict(gridcolor='rgba(128,128,128,0.15)', tickprefix='$'),
|
|
legend=dict(bgcolor='rgba(0,0,0,0)')
|
|
)
|
|
st.plotly_chart(fig, use_container_width=True)
|
|
|
|
def render_monthly_table(df_plot, label="Monthly Performance", key_prefix="mt"):
|
|
import pandas as pd
|
|
if 'close_time' not in df_plot.columns or df_plot.empty:
|
|
return
|
|
tmp = df_plot[['close_time','net_profit']].dropna().copy()
|
|
tmp['year'] = pd.to_datetime(tmp['close_time']).dt.year
|
|
tmp['month'] = pd.to_datetime(tmp['close_time']).dt.month
|
|
monthly = tmp.groupby(['year','month'])['net_profit'].sum().reset_index()
|
|
if monthly.empty:
|
|
return
|
|
|
|
pivot = monthly.pivot(index='year', columns='month', values='net_profit').fillna(0)
|
|
pivot.columns = [pd.Timestamp(2000, int(m), 1).strftime('%b') for m in pivot.columns]
|
|
pivot['YTD'] = pivot.sum(axis=1)
|
|
pivot = pivot.sort_index(ascending=False)
|
|
|
|
# Deposit for % calc — use initial deposit from session state or fallback to first equity point
|
|
deposit = st.session_state.get('ta_deposit', 10000.0)
|
|
|
|
tog1, tog2 = st.columns([2, 3])
|
|
toggle = tog1.radio("Unit", ["$", "%"], 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
|
|
|
|
month_order = ['Jan','Feb','Mar','Apr','May','Jun',
|
|
'Jul','Aug','Sep','Oct','Nov','Dec','YTD']
|
|
cols_present = [c for c in month_order if c in pivot.columns]
|
|
display = pivot[cols_present].copy()
|
|
|
|
if toggle == "%":
|
|
display = (display / deposit * 100).round(2)
|
|
|
|
# Build HTML table with colour coding
|
|
def _cell(val, fmt):
|
|
if val > 0: bg = "rgba(52,194,122,0.18)"; fg = "#34C27A"
|
|
elif val < 0: bg = "rgba(220,80,80,0.18)"; fg = "#E05555"
|
|
else: bg = "transparent"; fg = "#888"
|
|
txt = f"{val:+.2f}{'%' if fmt=='%' else ''}" if val != 0 else "—"
|
|
return f'<td style="background:{bg};color:{fg};padding:5px 10px;text-align:right;font-size:12px;font-family:monospace;border-bottom:1px solid rgba(128,128,128,0.1)">{txt}</td>'
|
|
|
|
rows = []
|
|
for year, row in display.iterrows():
|
|
cells = [f'<td style="padding:5px 10px;font-size:12px;font-weight:600;border-bottom:1px solid rgba(128,128,128,0.1)">{year}</td>']
|
|
for col in cols_present:
|
|
cells.append(_cell(row.get(col, 0), toggle))
|
|
rows.append("<tr>" + "".join(cells) + "</tr>")
|
|
|
|
hdr_cells = ["<th style='padding:5px 10px;font-size:11px;color:#888;text-align:right;border-bottom:1px solid rgba(128,128,128,0.2)'>Year</th>"]
|
|
for col in cols_present:
|
|
hdr_cells.append(f"<th style='padding:5px 10px;font-size:11px;color:#888;text-align:right;border-bottom:1px solid rgba(128,128,128,0.2)'>{col}</th>")
|
|
|
|
html = (
|
|
"<div style='overflow-x:auto'>"
|
|
"<table style='width:100%;border-collapse:collapse'>"
|
|
"<thead><tr>" + "".join(hdr_cells) + "</tr></thead>"
|
|
"<tbody>" + "".join(rows) + "</tbody>"
|
|
"</table></div>"
|
|
)
|
|
if label:
|
|
st.markdown(f"**{label}**")
|
|
st.markdown(html, unsafe_allow_html=True)
|
|
|
|
def colour_profit(val):
|
|
try:
|
|
v = float(str(val).replace(',', ''))
|
|
if v > 0: return 'background-color: rgba(0,180,0,0.12)'
|
|
if v < 0: return 'background-color: rgba(180,0,0,0.12)'
|
|
except:
|
|
pass
|
|
return ''
|
|
|
|
# ── Render mode ───────────────────────────────────────────────────────────
|
|
if mode == "Overall":
|
|
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_charts)
|
|
with col2:
|
|
render_hour_chart(_df_charts)
|
|
st.divider()
|
|
# ── 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())
|
|
if not strats:
|
|
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_s[_df_s['strategy'] == s] if s in _df_s['strategy'].values else df[df['strategy']==s]
|
|
stat = calc_stats(sdf)
|
|
row = {
|
|
'Strategy' : s,
|
|
'Trades' : stat['total_trades'],
|
|
'Net Profit' : stat['net_profit'],
|
|
'Win Rate %' : stat['win_rate'],
|
|
'Profit Factor' : stat['profit_factor'],
|
|
'R:R' : stat['rr_ratio'],
|
|
'Expectancy' : stat['expectancy'],
|
|
'Max DD' : stat['max_drawdown'],
|
|
'Max Consec W' : stat['max_consec_wins'],
|
|
'Max Consec L' : stat['max_consec_losses'],
|
|
}
|
|
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_s[_df_s['strategy'] == sel] if sel in _df_s['strategy'].values else df[df['strategy']==sel]
|
|
stat = calc_stats(sdf)
|
|
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()
|
|
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())
|
|
rows = []
|
|
for s in syms:
|
|
sdf = df[df['symbol'] == s]
|
|
stat = calc_stats(sdf)
|
|
rows.append({
|
|
'Symbol' : s,
|
|
'Trades' : stat['total_trades'],
|
|
'Net Profit' : stat['net_profit'],
|
|
'Win Rate %' : stat['win_rate'],
|
|
'Profit Factor' : stat['profit_factor'],
|
|
'R:R' : stat['rr_ratio'],
|
|
'Expectancy' : stat['expectancy'],
|
|
'Max DD' : stat['max_drawdown'],
|
|
})
|
|
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_sym[_df_sym['symbol'] == sel] if sel in _df_sym['symbol'].values else df[df['symbol']==sel]
|
|
stat = calc_stats(sdf)
|
|
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()
|
|
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":
|
|
_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"):
|
|
edit_cols = ['open_time', 'close_time', 'symbol', 'type', 'strategy',
|
|
'volume', 'open_price', 'close_price', 'sl', 'tp',
|
|
'commission', 'swap', 'profit', 'net_profit', 'duration_min']
|
|
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:
|
|
v = float(val)
|
|
if v > 0: return 'background-color: rgba(0,180,0,0.12)'
|
|
if v < 0: return 'background-color: rgba(180,0,0,0.12)'
|
|
except:
|
|
pass
|
|
return ''
|
|
|
|
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(
|
|
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'
|
|
) |