unknown
a784d26875
Trade Analysis: - Add toggleable equity curve overlays: Account total, By Strategy, By Symbol, By Day of Week — checkboxes above each chart - Fix all chart backgrounds to transparent (inherit page theme) - Fix grid and font colours to work on both dark and light backgrounds Settings page: - Full theme control: Dark / Light / Custom radio with Apply button - Custom mode: colour pickers for accent, page bg, sidebar bg, text, font selector and base dark/light toggle - Preview swatches before applying - Discard / Reset to Dark button - Text size: Normal / Large (+2px) / Extra Large (+4px) via CSS injection - inject_theme_css() helper for app.py to apply font size on all pages - Theme written to .streamlit/config.toml on apply
479 lines
22 KiB
Python
479 lines
22 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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}.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_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.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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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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# ── Filters ───────────────────────────────────────────────────────────────
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st.divider()
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fc1, fc2, fc3, fc4 = st.columns(4)
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with fc1:
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date_min = df_all['open_time'].min().date()
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date_max = df_all['open_time'].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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# Apply filters
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df = df_all.copy()
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df = df[(df['open_time'].dt.date >= date_from) &
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(df['open_time'].dt.date <= date_to)]
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if sel_symbol:
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df = df[df['symbol'].isin(sel_symbol)]
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if sel_strategy:
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df = df[df['strategy'].isin(sel_strategy)]
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if sel_days:
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df = df[df['day_of_week'].isin(sel_days)]
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if sel_type:
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df = df[df['type'].isin(sel_type)]
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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=""):
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if label:
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st.markdown(f"**{label}**")
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c1, c2, c3, c4, c5 = st.columns(5)
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c1.metric("Net Profit", f"${stats['net_profit']:,.2f}")
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c2.metric("Win Rate", f"{stats['win_rate']}%")
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c3.metric("Profit Factor", f"{stats['profit_factor']}")
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c4.metric("R:R Ratio", f"{stats['rr_ratio']}")
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c5.metric("Expectancy", f"${stats['expectancy']:,.2f}")
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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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c2.metric("Avg Win", f"${stats['avg_win']:,.2f}")
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c3.metric("Avg Loss", f"${stats['avg_loss']:,.2f}")
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c4.metric("Max DD", f"${stats['max_drawdown']:,.2f}")
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c5.metric("Best Trade", f"${stats['best_trade']:,.2f}")
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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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c2.metric("Max Consec Losses", stats['max_consec_losses'])
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c3.metric("Avg Win Dur", f"{stats['avg_win_duration']}m")
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c4.metric("Avg Loss Dur", f"{stats['avg_loss_duration']}m")
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c5.metric("Worst Trade", f"${stats['worst_trade']:,.2f}")
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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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c2.metric("Long Win Rate", f"{stats['long_win_rate']}%")
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c3.metric("Short Trades", stats['short_trades'])
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c4.metric("Short Win Rate",f"{stats['short_win_rate']}%")
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def render_equity_curve(df_plot, label="Equity Curve"):
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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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# ── Overlay toggles ───────────────────────────────────────────────
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ov_cols = st.columns(4)
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show_account = ov_cols[0].checkbox("Account total", value=True, key=f"eq_account_{label}")
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show_strategy = ov_cols[1].checkbox("By Strategy", value=False, key=f"eq_strat_{label}")
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show_symbol = ov_cols[2].checkbox("By Symbol", value=False, key=f"eq_sym_{label}")
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show_dow = ov_cols[3].checkbox("By Day of Week", value=False, key=f"eq_dow_{label}")
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COLORS = ['#7c6af7','#34C27A','#F5A623','#E05555','#4C8EF5',
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'#A78BFA','#22D3EE','#FB923C','#F472B6','#86EFAC']
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fig = go.Figure()
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# Account total
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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(
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x=df_s['close_time'], y=df_s['_cum'],
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mode='lines', name='Account',
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line=dict(color='#7c6af7', width=2),
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fill='tozeroy', fillcolor='rgba(124,106,247,0.06)',
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))
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# By Strategy
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if show_strategy and 'strategy' in df_s.columns:
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for i, strat in enumerate(sorted(df_s['strategy'].dropna().unique())):
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sub = df_s[df_s['strategy']==strat].copy()
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sub['_cum'] = sub['net_profit'].cumsum()
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fig.add_trace(go.Scatter(
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x=sub['close_time'], y=sub['_cum'],
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mode='lines', name=strat,
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line=dict(color=COLORS[(i+1)%len(COLORS)], width=1.5, dash='dot'),
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))
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# By Symbol
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if show_symbol and 'symbol' in df_s.columns:
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for i, sym in enumerate(sorted(df_s['symbol'].dropna().unique())):
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sub = df_s[df_s['symbol']==sym].copy()
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sub['_cum'] = sub['net_profit'].cumsum()
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fig.add_trace(go.Scatter(
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x=sub['close_time'], y=sub['_cum'],
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mode='lines', name=sym,
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line=dict(color=COLORS[(i+2)%len(COLORS)], width=1.5, dash='dash'),
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))
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# By Day of Week
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if show_dow and 'day_of_week' in df_s.columns:
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dow_order = ['Monday','Tuesday','Wednesday','Thursday','Friday']
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for i, dow in enumerate(dow_order):
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sub = df_s[df_s['day_of_week']==dow].copy()
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if sub.empty: continue
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sub['_cum'] = sub['net_profit'].cumsum()
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fig.add_trace(go.Scatter(
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x=sub['close_time'], y=sub['_cum'],
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mode='lines', name=dow,
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line=dict(color=COLORS[(i+3)%len(COLORS)], width=1.5),
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))
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fig.update_layout(
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title=label, height=360,
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plot_bgcolor='rgba(0,0,0,0)',
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paper_bgcolor='rgba(0,0,0,0)',
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font=dict(family='sans-serif'),
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xaxis=dict(gridcolor='rgba(128,128,128,0.15)', showgrid=True),
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yaxis=dict(gridcolor='rgba(128,128,128,0.15)', tickprefix='$', showgrid=True),
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margin=dict(l=60, r=20, t=40, b=40),
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legend=dict(bgcolor='rgba(0,0,0,0)', borderwidth=0),
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hovermode='x unified',
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)
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st.plotly_chart(fig, use_container_width=True, key=f"eq_fig_{label}")
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def render_dow_chart(df_plot):
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dow_order = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
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dow = df_plot.groupby('day_of_week').agg(
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trades = ('net_profit', 'count'),
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net_profit = ('net_profit', 'sum'),
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win_rate = ('win', lambda x: round(x.mean()*100, 1))
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).reindex([d for d in dow_order if d in df_plot['day_of_week'].unique()])
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wins_dow = df_plot[df_plot['win']].groupby('day_of_week')['net_profit'].sum().reindex(dow.index, fill_value=0)
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losses_dow = df_plot[~df_plot['win']].groupby('day_of_week')['net_profit'].sum().reindex(dow.index, fill_value=0)
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fig = go.Figure()
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fig.add_trace(go.Bar(x=dow.index, y=wins_dow, name='Profit', marker_color='rgba(45,198,83,0.8)'))
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fig.add_trace(go.Bar(x=dow.index, y=losses_dow, name='Loss', marker_color='rgba(230,57,70,0.8)'))
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fig.update_layout(
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title='P&L by Day of Week', height=280, barmode='relative',
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plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)',
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font=dict(family='sans-serif'), margin=dict(l=60, r=20, t=40, b=40),
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xaxis=dict(gridcolor='rgba(128,128,128,0.15)'),
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yaxis=dict(gridcolor='rgba(128,128,128,0.15)', tickprefix='$'),
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legend=dict(bgcolor='rgba(0,0,0,0.3)')
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)
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st.plotly_chart(fig, use_container_width=True)
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dt = dow.reset_index()
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dt.columns = ['Day', 'Trades', 'Net Profit', 'Win Rate %']
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dt['Net Profit'] = dt['Net Profit'].round(2)
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st.dataframe(dt, use_container_width=True, hide_index=True)
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def render_hour_chart(df_plot):
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hourly = df_plot.groupby('hour').agg(
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trades = ('net_profit', 'count'),
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net_profit = ('net_profit', 'sum'),
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)
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wins_h = df_plot[df_plot['win']].groupby('hour')['net_profit'].sum().reindex(hourly.index, fill_value=0)
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losses_h = df_plot[~df_plot['win']].groupby('hour')['net_profit'].sum().reindex(hourly.index, fill_value=0)
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fig = go.Figure()
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fig.add_trace(go.Bar(x=wins_h.index, y=wins_h, name='Profit', marker_color='rgba(45,198,83,0.8)'))
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fig.add_trace(go.Bar(x=losses_h.index, y=losses_h, name='Loss', marker_color='rgba(230,57,70,0.8)'))
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fig.update_layout(
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title='P&L by Hour of Day', height=280, barmode='relative',
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plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)',
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font=dict(family='sans-serif'), margin=dict(l=60, r=20, t=40, b=40),
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xaxis=dict(gridcolor='rgba(128,128,128,0.15)', title='Hour (UTC)'),
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yaxis=dict(gridcolor='rgba(128,128,128,0.15)', tickprefix='$'),
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legend=dict(bgcolor='rgba(0,0,0,0.3)')
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)
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st.plotly_chart(fig, use_container_width=True)
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def colour_profit(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(0,180,0,0.12)'
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if v < 0: return 'background-color: rgba(180,0,0,0.12)'
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except:
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pass
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return ''
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# ── Render mode ───────────────────────────────────────────────────────────
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if mode == "Overall":
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stats = calc_stats(df)
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render_stats(stats, "Overall Statistics")
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render_equity_curve(df)
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col1, col2 = st.columns(2)
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with col1:
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render_dow_chart(df)
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with col2:
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render_hour_chart(df)
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elif mode == "By Strategy":
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strats = sorted(df['strategy'].dropna().unique().tolist())
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if not strats:
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st.info("No strategies found")
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else:
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st.subheader("Strategy Comparison")
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rows = []
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for s in strats:
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sdf = df[df['strategy'] == s]
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stat = calc_stats(sdf)
|
|
rows.append({
|
|
'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'],
|
|
})
|
|
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
|
|
)
|
|
st.divider()
|
|
sel = st.selectbox("Select strategy for detail", strats)
|
|
if sel:
|
|
sdf = df[df['strategy'] == sel]
|
|
stat = calc_stats(sdf)
|
|
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)
|
|
|
|
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'],
|
|
})
|
|
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
|
|
)
|
|
sel = st.selectbox("Select symbol for detail", syms)
|
|
if sel:
|
|
sdf = df[df['symbol'] == sel]
|
|
stat = calc_stats(sdf)
|
|
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)
|
|
|
|
elif mode == "By Day of Week":
|
|
render_dow_chart(df)
|
|
render_hour_chart(df)
|
|
|
|
# ── Raw trade log ─────────────────────────────────────────────────────────
|
|
st.divider()
|
|
with st.expander("Raw Trade Log"):
|
|
show_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]
|
|
|
|
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.dataframe(
|
|
df[show_cols].style.map(colour_net, subset=['net_profit', 'profit']),
|
|
use_container_width=True, hide_index=True, height=400
|
|
)
|
|
st.download_button(
|
|
"⬇ Download filtered trades CSV",
|
|
data = df[show_cols].to_csv(index=False),
|
|
file_name = f"mt5_trades_{date_from}_{date_to}.csv",
|
|
mime = 'text/csv'
|
|
) |