Initial commit - MT5 Tools dashboard
This commit is contained in:
unknown
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# MT5 Tools
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Standalone Streamlit dashboard for MT5 trade analysis and comparison.
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## Setup
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```powershell
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cd C:\Users\pc\MT5Tools
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pip install -r requirements.txt
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```
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## Launch
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```powershell
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streamlit run app.py
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```
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## Files
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```
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MT5Tools\
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├── app.py ← main Streamlit app
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├── mt5_parser.py ← parsers for all 3 formats + stats
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├── mt5_batch_backtest.py ← batch backtest runner (copy from ea\)
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├── set_comparator.py ← EA set file comparator (copy from ea\)
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├── requirements.txt
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├── mt5_batch_config.json ← gitignored, created on first run
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└── pages\
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├── trade_analysis.py ← single report analysis
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├── trade_compare.py ← side-by-side comparison
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└── settings.py
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```
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## Supported Formats
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| Format | Extension | Notes |
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|---|---|---|
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| MT5 Account History | `.htm` / `.html` | Export from MT5 → Account History → Save as Report |
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| MT5 Backtest Report | `.htm` / `.html` | Generated by batch backtest runner or manual tester |
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| Quant Analyzer CSV | `.csv` | Export listOfTrades from Quant Analyzer |
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## .gitignore
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Add the following to `.gitignore`:
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```
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mt5_batch_config.json
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```
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"""
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MT5 Tools Dashboard
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===================
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Streamlit app for MT5 trade analysis and comparison.
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Launch: streamlit run app.py
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"""
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import streamlit as st
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from streamlit_option_menu import option_menu
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import importlib, sys, os
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# ── Page config ───────────────────────────────────────────────────────────────
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st.set_page_config(
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page_title = "MT5 Tools",
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page_icon = "📈",
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layout = "wide",
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initial_sidebar_state = "expanded"
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)
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# ── Theme ─────────────────────────────────────────────────────────────────────
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st.markdown("""
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<style>
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/* Base */
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@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;500;700&family=Syne:wght@400;600;800&display=swap');
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html, body, [class*="css"] {
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font-family: 'Syne', sans-serif;
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background-color: #0a0a0f;
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color: #e0e0e8;
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}
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code, .mono { font-family: 'JetBrains Mono', monospace; }
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/* Sidebar */
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[data-testid="stSidebar"] {
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background: linear-gradient(180deg, #0d0d1a 0%, #0a0a12 100%);
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border-right: 1px solid rgba(255,255,255,0.06);
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}
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[data-testid="stSidebar"] .stMarkdown h3 {
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color: #7c6af7;
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font-size: 11px;
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letter-spacing: 0.15em;
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text-transform: uppercase;
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font-weight: 800;
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}
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/* Cards */
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.stat-card {
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background: rgba(255,255,255,0.03);
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border: 1px solid rgba(255,255,255,0.07);
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border-radius: 8px;
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padding: 14px 18px;
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margin-bottom: 8px;
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}
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.stat-label {
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font-size: 10px;
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letter-spacing: 0.1em;
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text-transform: uppercase;
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color: #666;
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margin-bottom: 4px;
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}
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.stat-value {
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font-family: 'JetBrains Mono', monospace;
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font-size: 22px;
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font-weight: 700;
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color: #e0e0e8;
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}
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.stat-pos { color: #2dc653; }
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.stat-neg { color: #e63946; }
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/* Info card */
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.info-card {
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background: rgba(124,106,247,0.08);
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border: 1px solid rgba(124,106,247,0.2);
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border-radius: 8px;
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padding: 12px 16px;
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font-size: 13px;
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color: #aaa;
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margin: 8px 0;
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}
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/* Comparison cells */
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.diff-pos { color: #2dc653; font-weight: 600; }
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.diff-neg { color: #e63946; font-weight: 600; }
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.diff-neu { color: #888; }
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/* Divider */
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hr { border-color: rgba(255,255,255,0.06); }
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/* Metric overrides */
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[data-testid="metric-container"] {
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background: rgba(255,255,255,0.02);
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border: 1px solid rgba(255,255,255,0.06);
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border-radius: 8px;
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padding: 12px;
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}
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[data-testid="stMetricValue"] {
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font-family: 'JetBrains Mono', monospace;
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font-size: 18px !important;
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}
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/* Buttons */
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.stButton > button {
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background: rgba(124,106,247,0.15);
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border: 1px solid rgba(124,106,247,0.3);
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color: #c5beff;
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font-family: 'Syne', sans-serif;
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font-weight: 600;
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letter-spacing: 0.05em;
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border-radius: 6px;
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transition: all 0.15s;
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}
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.stButton > button:hover {
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background: rgba(124,106,247,0.3);
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border-color: rgba(124,106,247,0.6);
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}
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/* File uploader */
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[data-testid="stFileUploader"] {
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background: rgba(255,255,255,0.02);
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border: 1px dashed rgba(255,255,255,0.12);
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border-radius: 8px;
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}
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/* Tab overrides */
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.stTabs [data-baseweb="tab"] {
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font-family: 'Syne', sans-serif;
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font-weight: 600;
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font-size: 13px;
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}
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/* Scrollbar */
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::-webkit-scrollbar { width: 6px; height: 6px; }
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::-webkit-scrollbar-track { background: transparent; }
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::-webkit-scrollbar-thumb { background: rgba(255,255,255,0.1); border-radius: 3px; }
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</style>
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""", unsafe_allow_html=True)
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# ── Sidebar nav ───────────────────────────────────────────────────────────────
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with st.sidebar:
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st.markdown("### 📈 MT5 Tools")
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st.markdown("---")
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page = option_menu(
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menu_title = None,
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options = ["Trade Analysis", "Trade Compare", "EA Comparator", "Settings"],
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icons = ["bar-chart-line", "arrow-left-right", "sliders", "gear"],
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default_index = 0,
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styles = {
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"container" : {"background-color": "transparent", "padding": "0"},
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"icon" : {"color": "#7c6af7", "font-size": "14px"},
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"nav-link" : {
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"font-family" : "Syne, sans-serif",
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"font-size" : "13px",
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"font-weight" : "600",
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"color" : "#aaa",
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"border-radius": "6px",
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"margin" : "2px 0",
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},
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"nav-link-selected": {
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"background-color": "rgba(124,106,247,0.15)",
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"color" : "#c5beff",
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"border" : "1px solid rgba(124,106,247,0.25)",
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},
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}
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)
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# ── Route pages ───────────────────────────────────────────────────────────────
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if page == "Trade Analysis":
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import view_trade_analysis as p
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importlib.reload(p)
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p.render()
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elif page == "Trade Compare":
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import view_trade_compare as p
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importlib.reload(p)
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p.render()
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elif page == "EA Comparator":
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import view_set_comparator as p
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importlib.reload(p)
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p.render()
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elif page == "Settings":
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import view_settings as p
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importlib.reload(p)
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p.render()
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"""
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MT5 Batch Backtest Runner
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=========================
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Generates a MT5 tester .ini file for each .set file in a folder,
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updates EA_Comment in each .set file, then launches MT5 terminal
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for each backtest sequentially.
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On first run: detects MT5 installations and saves config.
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Subsequent runs: loads saved config, prompts to use same or modify.
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Usage: python mt5_batch_backtest.py
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"""
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import os
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import sys
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import glob
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import subprocess
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import time
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import shutil
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import json
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# ── MT5 Period constants ───────────────────────────────────────────────────────
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PERIOD_MAP = {
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'M1' : 'M1',
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'M5' : 'M5',
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'M15' : 'M15',
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'M30' : 'M30',
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'H1' : 'H1',
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'H4' : 'H4',
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'D' : 'Daily',
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'D1' : 'Daily',
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'DAILY': 'Daily',
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'W1' : 'Weekly',
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'MN' : 'Monthly',
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}
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# ── Model labels ─────────────────────────────────────────────────────────────
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MODEL_LABELS = {
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'1' : 'OHLC',
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'2' : 'CTRLPTS',
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'4' : 'EVERYTICK',
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'5' : 'EVERYTICKREAL',
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}
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# ── Defaults (used if no config found) ────────────────────────────────────────
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DEFAULTS = {
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'terminal_path' : r"C:\Program Files\MetaTrader 5\terminal64.exe",
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'tester_folder' : '',
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'ea_name' : r"Market\Ultimate Breakout System.ex5",
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'from_date' : '2018.01.01',
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'to_date' : '2026.04.01',
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'model' : '1',
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'deposit' : '10000',
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'currency' : 'USD',
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'leverage' : '100',
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'optimization' : '0',
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'suffix' : '.a',
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}
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CONFIG_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'mt5_batch_config.json')
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# ── Helpers ────────────────────────────────────────────────────────────────────
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def prompt(text, default=None):
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if default is not None and default != '':
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val = input(f" {text} [{default}]: ").strip()
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return val if val else default
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else:
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while True:
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val = input(f" {text}: ").strip()
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if val:
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return val
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print(" (required)")
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def load_config():
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if os.path.isfile(CONFIG_FILE):
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try:
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with open(CONFIG_FILE, 'r') as f:
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return json.load(f)
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except:
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pass
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return None
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def save_config(cfg):
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try:
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with open(CONFIG_FILE, 'w') as f:
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json.dump(cfg, f, indent=2)
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except Exception as e:
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print(f" WARNING: Could not save config: {e}")
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def find_mt5_terminals():
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"""Scan MetaQuotes Terminal folder for all MT5 installations."""
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appdata = os.environ.get('APPDATA', '')
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base = os.path.join(appdata, 'MetaQuotes', 'Terminal')
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results = []
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if not os.path.isdir(base):
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return results
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for entry in os.listdir(base):
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entry_path = os.path.join(base, entry)
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if not os.path.isdir(entry_path):
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continue
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# Check for origin.txt which contains the terminal exe path
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origin = os.path.join(entry_path, 'origin.txt')
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tester = os.path.join(entry_path, 'Tester')
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label = entry
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if os.path.isfile(origin):
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try:
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with open(origin, 'r', encoding='utf-8', errors='replace') as f:
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label = f.read().strip() or entry
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except:
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pass
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# Skip folders that don't look like real MT5 terminals
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if not os.path.isdir(os.path.join(entry_path, 'MQL5')):
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continue
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results.append({
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'id' : entry,
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'label' : label,
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'tester_folder' : tester,
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'data_folder' : entry_path,
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})
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return results
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def pick_terminal():
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"""Let user pick from detected MT5 terminals."""
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terminals = find_mt5_terminals()
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if not terminals:
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print(" No MT5 terminals found in AppData\\MetaQuotes\\Terminal\\")
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print(" You will need to enter the tester folder path manually.")
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return None, None
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print()
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print(" Detected MT5 terminal(s):")
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for i, t in enumerate(terminals, 1):
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print(f" {i}) {t['label']}")
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print(f" Tester: {t['tester_folder']}")
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if len(terminals) == 1:
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choice = input(f"\n Select terminal [1]: ").strip()
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idx = 0
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else:
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while True:
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choice = input(f"\n Select terminal [1-{len(terminals)}]: ").strip()
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try:
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idx = int(choice) - 1
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if 0 <= idx < len(terminals):
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break
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except:
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pass
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print(" Invalid selection.")
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selected = terminals[idx]
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return selected['tester_folder'], selected['label']
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def find_ea_files(tester_folder):
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"""
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Scan MQL5/Experts folder for .ex5 files.
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Returns list of dicts with label (display) and value (ini path).
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"""
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experts_dir = os.path.join(os.path.dirname(tester_folder), 'MQL5', 'Experts')
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results = []
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if not os.path.isdir(experts_dir):
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return results
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for root, dirs, files in os.walk(experts_dir):
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dirs[:] = [d for d in dirs if not d.startswith('.')]
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for fn in sorted(files):
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if fn.lower().endswith('.ex5'):
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full_path = os.path.join(root, fn)
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rel = os.path.relpath(full_path, experts_dir)
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# Only include EAs in the Market subfolder
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if rel.startswith('Market' + os.sep) or rel.startswith('Market/'):
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results.append({'label': rel, 'value': rel})
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return results
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def pick_ea_name(tester_folder, current=None):
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"""List available EAs and let user pick, or enter manually."""
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eas = find_ea_files(tester_folder)
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if not eas:
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print(" No .ex5 files found in MQL5/Experts -- enter EA name manually.")
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return prompt("EA Name", current or DEFAULTS['ea_name'])
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print()
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print(" Available EAs:")
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for i, ea in enumerate(eas, 1):
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marker = ' <' if current and ea['value'] == current else ''
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print(f" {i:>3}) {ea['label']}{marker}")
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print(f" {len(eas)+1:>3}) Enter manually")
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while True:
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default_idx = None
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if current:
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||||
for i, ea in enumerate(eas, 1):
|
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if ea['value'] == current:
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default_idx = i
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break
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hint = f"1-{len(eas)+1}" + (f", Enter={default_idx}" if default_idx else "")
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choice = input(f" Select EA [{hint}]: ").strip()
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if choice == '' and default_idx:
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return eas[default_idx - 1]['value']
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try:
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idx = int(choice) - 1
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||||
if idx == len(eas):
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return prompt("EA Name", current or DEFAULTS['ea_name'])
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||||
if 0 <= idx < len(eas):
|
||||
return eas[idx]['value']
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||||
except:
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pass
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print(" Invalid selection.")
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||||
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||||
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def setup_config():
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||||
"""First-run setup — detect terminals and build config."""
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print()
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print(" ── First Run Setup ──────────────────────────────────────")
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||||
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tester_folder, terminal_label = pick_terminal()
|
||||
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if not tester_folder:
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tester_folder = prompt("Tester folder path")
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||||
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||||
terminal_path = prompt("Path to terminal64.exe", DEFAULTS['terminal_path'])
|
||||
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||||
print()
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||||
print(" ── Backtest Defaults ────────────────────────────────────")
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cfg = {
|
||||
'terminal_path' : terminal_path,
|
||||
'tester_folder' : tester_folder,
|
||||
'terminal_label': terminal_label or '',
|
||||
'ea_name' : pick_ea_name(tester_folder, DEFAULTS['ea_name']),
|
||||
'from_date' : prompt("From Date (YYYY.MM.DD)", DEFAULTS['from_date']),
|
||||
'to_date' : prompt("To Date (YYYY.MM.DD)", DEFAULTS['to_date']),
|
||||
'model' : prompt("Model (1=OHLC M1, 2=Control points, 4=Every tick)", DEFAULTS['model']),
|
||||
'deposit' : prompt("Deposit", DEFAULTS['deposit']),
|
||||
'currency' : prompt("Currency", DEFAULTS['currency']),
|
||||
'leverage' : prompt("Leverage", DEFAULTS['leverage']),
|
||||
'suffix' : prompt("Instrument suffix (e.g. .a)", DEFAULTS['suffix']),
|
||||
}
|
||||
save_config(cfg)
|
||||
print()
|
||||
print(f" Config saved to: {CONFIG_FILE}")
|
||||
return cfg
|
||||
|
||||
|
||||
def review_config(cfg):
|
||||
"""Show saved config and ask to use same or modify."""
|
||||
print()
|
||||
print(" ── Saved Settings ───────────────────────────────────────")
|
||||
print(f" Terminal : {cfg.get('terminal_label', cfg['tester_folder'])}")
|
||||
print(f" Tester : {cfg['tester_folder']}")
|
||||
print(f" EA : {cfg['ea_name']}")
|
||||
print(f" Dates : {cfg['from_date']} → {cfg['to_date']}")
|
||||
print(f" Model : {cfg['model']} Deposit: {cfg['deposit']} {cfg['currency']} Leverage: {cfg['leverage']}")
|
||||
print(f" Suffix : {cfg['suffix']}")
|
||||
print()
|
||||
choice = input(" Use these settings? [Y/n/reset]: ").strip().lower()
|
||||
|
||||
if choice == 'reset':
|
||||
os.remove(CONFIG_FILE)
|
||||
print(" Config reset — re-running setup.")
|
||||
return setup_config()
|
||||
|
||||
if choice == 'n':
|
||||
print()
|
||||
print(" ── Modify Settings ──────────────────────────────────────")
|
||||
redetect = input(" Re-detect MT5 terminals? [y/N]: ").strip().lower()
|
||||
if redetect == 'y':
|
||||
tester_folder, terminal_label = pick_terminal()
|
||||
if tester_folder:
|
||||
cfg['tester_folder'] = tester_folder
|
||||
cfg['terminal_label'] = terminal_label or ''
|
||||
|
||||
cfg['terminal_path'] = prompt("terminal64.exe path", cfg['terminal_path'])
|
||||
cfg['ea_name'] = pick_ea_name(cfg['tester_folder'], cfg['ea_name'])
|
||||
cfg['from_date'] = prompt("From Date", cfg['from_date'])
|
||||
cfg['to_date'] = prompt("To Date", cfg['to_date'])
|
||||
cfg['model'] = prompt("Model (1=OHLC M1, 2=Control points, 4=Every tick)", cfg['model'])
|
||||
cfg['deposit'] = prompt("Deposit", cfg['deposit'])
|
||||
cfg['currency'] = prompt("Currency", cfg['currency'])
|
||||
cfg['leverage'] = prompt("Leverage", cfg['leverage'])
|
||||
cfg['suffix'] = prompt("Suffix", cfg['suffix'])
|
||||
save_config(cfg)
|
||||
print(" Config updated.")
|
||||
|
||||
return cfg
|
||||
|
||||
|
||||
def detect_timeframe(filename):
|
||||
name = os.path.splitext(filename)[0].upper()
|
||||
for token in PERIOD_MAP:
|
||||
if name.endswith('_' + token) or name.endswith('-' + token) or \
|
||||
('_' + token + '_') in name or ('-' + token + '-') in name:
|
||||
return PERIOD_MAP[token]
|
||||
return None
|
||||
|
||||
|
||||
def detect_instrument(filename, n_chars):
|
||||
return os.path.splitext(filename)[0][:n_chars].upper()
|
||||
|
||||
|
||||
def read_utf16(path):
|
||||
with open(path, 'rb') as f:
|
||||
raw = f.read()
|
||||
if raw[:2] == b'\xff\xfe':
|
||||
text = raw[2:].decode('utf-16-le')
|
||||
elif raw[:2] == b'\xfe\xff':
|
||||
text = raw[2:].decode('utf-16-be')
|
||||
else:
|
||||
text = raw.decode('utf-8', errors='replace')
|
||||
return text.splitlines()
|
||||
|
||||
|
||||
def write_utf16(path, lines):
|
||||
text = '\r\n'.join(lines) + '\r\n'
|
||||
with open(path, 'wb') as f:
|
||||
f.write(b'\xff\xfe')
|
||||
f.write(text.encode('utf-16-le'))
|
||||
|
||||
|
||||
def update_set_file(set_path, ea_comment, lot_mode, lot_value):
|
||||
lines = read_utf16(set_path)
|
||||
|
||||
def update_param(lines, key, new_val):
|
||||
for i, line in enumerate(lines):
|
||||
if line.strip().startswith(key + '='):
|
||||
parts = line.strip().split('||')
|
||||
parts[0] = f'{key}={new_val}'
|
||||
lines[i] = '||'.join(parts)
|
||||
return True
|
||||
return False
|
||||
|
||||
# Always update EA_Comment
|
||||
updated = False
|
||||
for i, line in enumerate(lines):
|
||||
if line.strip().startswith('EA_Comment='):
|
||||
lines[i] = f'EA_Comment={ea_comment}'
|
||||
updated = True
|
||||
break
|
||||
if not updated:
|
||||
lines.append(f'EA_Comment={ea_comment}')
|
||||
|
||||
if lot_mode == 'manual':
|
||||
update_param(lines, 'Risk', '0')
|
||||
update_param(lines, 'StartLots', str(lot_value))
|
||||
elif lot_mode == 'balance':
|
||||
update_param(lines, 'Risk', '9999')
|
||||
update_param(lines, 'LotPerBalance_step', str(lot_value))
|
||||
# lot_mode None/'asis': only EA_Comment updated
|
||||
|
||||
write_utf16(set_path, lines)
|
||||
|
||||
|
||||
def build_ini(symbol, period, set_file_path, ini_out_path, report_folder, cfg):
|
||||
name_stem = os.path.splitext(os.path.basename(set_file_path))[0]
|
||||
model_label = MODEL_LABELS.get(cfg['model'], f"M{cfg['model']}")
|
||||
report_name = f"{name_stem}_{model_label}"
|
||||
content = (
|
||||
'[Tester]\r\n'
|
||||
f'Expert={cfg["ea_name"]}\r\n'
|
||||
f'Symbol={symbol}\r\n'
|
||||
f'Period={period}\r\n'
|
||||
f'Optimization={cfg.get("optimization","0")}\r\n'
|
||||
f'Model={cfg["model"]}\r\n'
|
||||
f'FromDate={cfg["from_date"]}\r\n'
|
||||
f'ToDate={cfg["to_date"]}\r\n'
|
||||
'ForwardMode=0\r\n'
|
||||
f'Deposit={cfg["deposit"]}\r\n'
|
||||
f'Currency={cfg["currency"]}\r\n'
|
||||
'ProfitInPips=0\r\n'
|
||||
f'Leverage={cfg["leverage"]}\r\n'
|
||||
'ExecutionMode=0\r\n'
|
||||
'OptimizationCriterion=0\r\n'
|
||||
'Visual=0\r\n'
|
||||
f'Report={report_name}\r\n'
|
||||
'ReplaceReport=1\r\n'
|
||||
f'Inputs={set_file_path}\r\n'
|
||||
'ShutdownTerminal=1\r\n'
|
||||
)
|
||||
with open(ini_out_path, 'wb') as f:
|
||||
f.write(content.encode('utf-8'))
|
||||
|
||||
|
||||
# ── Main ───────────────────────────────────────────────────────────────────────
|
||||
|
||||
def main():
|
||||
print("=" * 60)
|
||||
print(" MT5 Batch Backtest Runner")
|
||||
print("=" * 60)
|
||||
|
||||
# ── Load or create config ──────────────────────────────────────
|
||||
cfg = load_config()
|
||||
if cfg is None:
|
||||
cfg = setup_config()
|
||||
else:
|
||||
cfg = review_config(cfg)
|
||||
|
||||
terminal_path = cfg['terminal_path']
|
||||
tester_folder = cfg['tester_folder']
|
||||
os.makedirs(tester_folder, exist_ok=True)
|
||||
|
||||
if not os.path.isfile(terminal_path):
|
||||
print(f"\n WARNING: terminal64.exe not found at: {terminal_path}")
|
||||
|
||||
# ── Folder of set files ────────────────────────────────────────
|
||||
print()
|
||||
set_folder = prompt("Path to folder containing .set files")
|
||||
set_folder = os.path.expandvars(set_folder.strip('"').strip("'"))
|
||||
if not os.path.isdir(set_folder):
|
||||
print(f" ERROR: Folder not found: {set_folder}")
|
||||
sys.exit(1)
|
||||
|
||||
all_set_files = sorted(glob.glob(os.path.join(set_folder, '**', '*.set'), recursive=True))
|
||||
set_files = [f for f in all_set_files
|
||||
if not os.path.basename(f).lower().startswith('optimization')
|
||||
and '_batch_modified' not in f.replace('\\', '/')]
|
||||
skipped = len(all_set_files) - len(set_files)
|
||||
if not set_files:
|
||||
print(" ERROR: No .set files found (after exclusions).")
|
||||
sys.exit(1)
|
||||
print(f" Found {len(set_files)} .set file(s).", end='')
|
||||
if skipped:
|
||||
print(f" ({skipped} Optimization file(s) excluded)", end='')
|
||||
print()
|
||||
|
||||
# ── Report output folder ───────────────────────────────────────
|
||||
default_reports = os.path.join(set_folder, 'reports')
|
||||
report_folder = prompt("Path to save reports", default_reports)
|
||||
report_folder = report_folder.strip('"').strip("'")
|
||||
os.makedirs(report_folder, exist_ok=True)
|
||||
|
||||
# ── Lot size mode ──────────────────────────────────────────────
|
||||
print()
|
||||
print(" Lot size mode:")
|
||||
print(" 0 = Use set file as-is (no changes to Risk/Lots)")
|
||||
print(" 1 = Manual lot size (same for all) — sets Risk=0, StartLots=X")
|
||||
print(" 2 = Lots per balance (from each .set file) — sets Risk=9999")
|
||||
print(" 3 = Lots per balance (enter per file) — sets Risk=9999")
|
||||
lot_mode_choice = prompt("Choose [0/1/2/3]", "2")
|
||||
|
||||
lot_mode = {'0': 'asis', '1': 'manual'}.get(lot_mode_choice, 'balance')
|
||||
balance_ask = lot_mode_choice == '3'
|
||||
manual_lots = None
|
||||
|
||||
if lot_mode == 'asis':
|
||||
print(" Set files used as-is — no Risk/Lots changes.")
|
||||
elif lot_mode == 'manual':
|
||||
manual_lots = prompt("StartLots for all files", "0.01")
|
||||
elif balance_ask:
|
||||
print(" Will prompt LotPerBalance_step per file. Risk=9999.")
|
||||
else:
|
||||
print(" Using LotPerBalance_step from each file. Risk=9999.")
|
||||
|
||||
# ── Instrument mode ────────────────────────────────────────────
|
||||
print()
|
||||
print(" Instrument detection:")
|
||||
print(" 1 = Enter one instrument for all set files")
|
||||
print(" 2 = Extract from filename (specify number of characters)")
|
||||
print(" 3 = Ask per file")
|
||||
instr_mode = prompt("Choose [1/2/3]", "1")
|
||||
instr_global = None
|
||||
instr_n_chars = None
|
||||
|
||||
if instr_mode == '1':
|
||||
instr_global = prompt("Instrument (without suffix, e.g. GBPJPY)").upper()
|
||||
elif instr_mode == '2':
|
||||
instr_n_chars = int(prompt("Characters from start of filename", "6"))
|
||||
|
||||
# ── Timeframe mode ─────────────────────────────────────────────
|
||||
print()
|
||||
print(" Timeframe:")
|
||||
print(" 1 = One timeframe for all set files")
|
||||
print(" 2 = Detect from filename (D/Daily/H1/H4 etc.)")
|
||||
print(" 3 = Ask per file")
|
||||
tf_mode = prompt("Choose [1/2/3]", "2")
|
||||
tf_global = None
|
||||
|
||||
if tf_mode == '1':
|
||||
tf_raw = prompt("Timeframe (e.g. Daily, H1, H4, M15)").upper()
|
||||
tf_global = PERIOD_MAP.get(tf_raw, tf_raw)
|
||||
|
||||
# ── Output folder for modified .set copies ─────────────────────
|
||||
# (subfolders mirrored inside _batch_modified and reports)
|
||||
out_set_base = os.path.join(set_folder, '_batch_modified')
|
||||
os.makedirs(out_set_base, exist_ok=True)
|
||||
|
||||
# ── Process each set file ──────────────────────────────────────
|
||||
print()
|
||||
print("=" * 60)
|
||||
print(f" Processing {len(set_files)} file(s)...")
|
||||
print("=" * 60)
|
||||
|
||||
results = []
|
||||
|
||||
for set_path in set_files:
|
||||
filename = os.path.basename(set_path)
|
||||
name_stem = os.path.splitext(filename)[0]
|
||||
|
||||
# Mirror subfolder structure from set_folder root
|
||||
rel_path = os.path.relpath(set_path, set_folder)
|
||||
rel_subdir = os.path.dirname(rel_path)
|
||||
out_set_folder = os.path.join(out_set_base, rel_subdir) if rel_subdir else out_set_base
|
||||
file_report_dir = os.path.join(report_folder, rel_subdir) if rel_subdir else report_folder
|
||||
os.makedirs(out_set_folder, exist_ok=True)
|
||||
os.makedirs(file_report_dir, exist_ok=True)
|
||||
|
||||
subfolder_label = f" ({rel_subdir})" if rel_subdir else ""
|
||||
print(f"\n [{filename}]{subfolder_label}")
|
||||
|
||||
# Instrument
|
||||
if instr_mode == '1':
|
||||
instrument = instr_global
|
||||
elif instr_mode == '2':
|
||||
instrument = detect_instrument(filename, instr_n_chars)
|
||||
print(f" Instrument: {instrument}")
|
||||
else:
|
||||
instrument = prompt(f"Instrument for {filename} (without suffix)").upper()
|
||||
|
||||
symbol = instrument + cfg['suffix']
|
||||
|
||||
# Timeframe
|
||||
if tf_mode == '1':
|
||||
period = tf_global
|
||||
elif tf_mode == '2':
|
||||
period = detect_timeframe(filename)
|
||||
if period:
|
||||
print(f" Timeframe : {period}")
|
||||
else:
|
||||
tf_raw = prompt(f"Timeframe for {filename} (e.g. Daily, H1, H4)").upper()
|
||||
period = PERIOD_MAP.get(tf_raw, tf_raw)
|
||||
else:
|
||||
tf_raw = prompt(f"Timeframe for {filename}").upper()
|
||||
period = PERIOD_MAP.get(tf_raw, tf_raw)
|
||||
|
||||
# Lot value
|
||||
if lot_mode == 'asis':
|
||||
lot_value = None
|
||||
print(f" Lots : as-is")
|
||||
elif lot_mode == 'manual':
|
||||
lot_value = manual_lots
|
||||
print(f" Lots : Manual StartLots={lot_value}")
|
||||
else:
|
||||
if balance_ask:
|
||||
file_lines = read_utf16(set_path)
|
||||
file_lot = None
|
||||
for line in file_lines:
|
||||
if line.strip().startswith('LotPerBalance_step='):
|
||||
parts = line.strip().split('||')
|
||||
file_lot = parts[0].replace('LotPerBalance_step=', '').strip()
|
||||
break
|
||||
lot_value = prompt(f"LotPerBalance_step for {filename}", file_lot or "100")
|
||||
else:
|
||||
file_lines = read_utf16(set_path)
|
||||
lot_value = None
|
||||
for line in file_lines:
|
||||
if line.strip().startswith('LotPerBalance_step='):
|
||||
parts = line.strip().split('||')
|
||||
lot_value = parts[0].replace('LotPerBalance_step=', '').strip()
|
||||
break
|
||||
if lot_value is None:
|
||||
lot_value = prompt(f"LotPerBalance_step not found, enter value", "100")
|
||||
print(f" Lots : Balance LotPerBalance_step={lot_value} Risk=9999")
|
||||
|
||||
# EA_Comment
|
||||
model_label = MODEL_LABELS.get(cfg['model'], f"M{cfg['model']}")
|
||||
ea_comment = f"{name_stem} {symbol} {period} {model_label}"
|
||||
print(f" EA_Comment: {ea_comment}")
|
||||
|
||||
# Copy and modify set file
|
||||
modified_set = os.path.join(out_set_folder, filename)
|
||||
shutil.copy2(set_path, modified_set)
|
||||
if lot_mode == 'asis':
|
||||
update_set_file(modified_set, ea_comment, None, None)
|
||||
else:
|
||||
update_set_file(modified_set, ea_comment, lot_mode, lot_value)
|
||||
|
||||
# Write ini
|
||||
model_label = MODEL_LABELS.get(cfg['model'], f"M{cfg['model']}")
|
||||
report_name = f"{name_stem}_{model_label}"
|
||||
ini_path = os.path.join(tester_folder, f"{name_stem}.ini")
|
||||
build_ini(symbol, period, modified_set, ini_path, report_folder, cfg)
|
||||
print(f" INI : {ini_path}")
|
||||
print(f" Report as : {report_name}.htm")
|
||||
|
||||
# Launch MT5 minimised
|
||||
cmd = [terminal_path, f'/config:{ini_path}']
|
||||
print(f" Launching MT5", end='', flush=True)
|
||||
si = subprocess.STARTUPINFO()
|
||||
si.dwFlags |= subprocess.STARTF_USESHOWWINDOW
|
||||
si.wShowWindow = 6 # SW_MINIMIZE
|
||||
proc = subprocess.Popen(cmd, startupinfo=si)
|
||||
while proc.poll() is None:
|
||||
time.sleep(10)
|
||||
print('.', end='', flush=True)
|
||||
print(f" done (exit {proc.returncode})")
|
||||
|
||||
# Copy report files from MT5 terminal folder
|
||||
search_dirs = [
|
||||
os.path.dirname(tester_folder),
|
||||
os.path.join(os.path.dirname(tester_folder), 'MQL5', 'Profiles', 'Tester'),
|
||||
tester_folder,
|
||||
]
|
||||
|
||||
success = False
|
||||
for search_dir in search_dirs:
|
||||
htm_src = os.path.join(search_dir, report_name + '.htm')
|
||||
if os.path.isfile(htm_src):
|
||||
htm_dest = os.path.join(file_report_dir, report_name + '.htm')
|
||||
shutil.copy2(htm_src, htm_dest)
|
||||
copied = [report_name + '.htm']
|
||||
for fn in os.listdir(search_dir):
|
||||
if fn.startswith(report_name) and not fn.endswith('.htm'):
|
||||
shutil.copy2(os.path.join(search_dir, fn),
|
||||
os.path.join(file_report_dir, fn))
|
||||
copied.append(fn)
|
||||
# Remove from MT5 folder
|
||||
os.remove(htm_src)
|
||||
for fn in os.listdir(search_dir):
|
||||
if fn.startswith(report_name) and not fn.endswith('.htm'):
|
||||
try:
|
||||
os.remove(os.path.join(search_dir, fn))
|
||||
except:
|
||||
pass
|
||||
print(f" Report : {len(copied)} file(s) → {file_report_dir}")
|
||||
success = True
|
||||
break
|
||||
|
||||
if not success:
|
||||
print(f" FAIL : Report not found. Checked:")
|
||||
for d in search_dirs:
|
||||
print(f" {d}")
|
||||
|
||||
results.append({
|
||||
'file' : filename,
|
||||
'subfolder': rel_subdir,
|
||||
'symbol' : symbol,
|
||||
'period' : period,
|
||||
'success' : success,
|
||||
})
|
||||
|
||||
# ── Summary ────────────────────────────────────────────────────
|
||||
print()
|
||||
print("=" * 60)
|
||||
print(" SUMMARY")
|
||||
print("=" * 60)
|
||||
passed = [r for r in results if r['success']]
|
||||
failed = [r for r in results if not r['success']]
|
||||
print(f" Completed: {len(passed)}/{len(results)}")
|
||||
if failed:
|
||||
print(f"\n Failed (no report generated):")
|
||||
for r in failed:
|
||||
subfolder = f" [{r['subfolder']}]" if r.get('subfolder') else ""
|
||||
print(f" - {r['file']}{subfolder} ({r['symbol']} {r['period']})")
|
||||
failed_log = os.path.join(report_folder, 'failed_backtests.txt')
|
||||
with open(failed_log, 'w') as f:
|
||||
for r in failed:
|
||||
f.write(f"{r['file']}\t{r['symbol']}\t{r['period']}\n")
|
||||
print(f"\n Failed list saved to: {failed_log}")
|
||||
print()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"terminal_path": "C:\\Program Files\\MetaTrader 5\\terminal64.exe",
|
||||
"tester_folder": "C:\\Users\\pc\\AppData\\Roaming\\MetaQuotes\\Terminal\\D0E8209F77C8CF37AD8BF550E51FF075\\Tester",
|
||||
"terminal_label": "\ufffd\ufffdC\u0000:\u0000\\\u0000P\u0000r\u0000o\u0000g\u0000r\u0000a\u0000m\u0000 \u0000F\u0000i\u0000l\u0000e\u0000s\u0000\\\u0000M\u0000e\u0000t\u0000a\u0000T\u0000r\u0000a\u0000d\u0000e\u0000r\u0000 \u00005\u0000",
|
||||
"ea_name": "Market\\Ultimate Breakout System.ex5",
|
||||
"from_date": "2018.01.01",
|
||||
"to_date": "2026.04.01",
|
||||
"model": "1",
|
||||
"deposit": "10000",
|
||||
"currency": "USD",
|
||||
"leverage": "100",
|
||||
"suffix": ".a"
|
||||
}
|
||||
+389
@@ -0,0 +1,389 @@
|
||||
"""
|
||||
mt5_parser.py
|
||||
=============
|
||||
Parsers for three MT5 trade report formats:
|
||||
1. MT5 Real Account HTM export
|
||||
2. MT5 Backtest HTM report
|
||||
3. Quant Analyzer CSV export
|
||||
|
||||
All normalise to a common DataFrame schema.
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import re
|
||||
|
||||
|
||||
# ── Common schema ─────────────────────────────────────────────────────────────
|
||||
# open_time, close_time, symbol, type, volume, open_price, close_price,
|
||||
# sl, tp, commission, swap, profit, net_profit, comment, strategy,
|
||||
# duration_min, win, day_of_week, hour, source
|
||||
|
||||
def _decode(file_bytes):
|
||||
for enc in ['utf-16', 'utf-8', 'latin-1', 'cp1252']:
|
||||
try:
|
||||
return file_bytes.decode(enc)
|
||||
except:
|
||||
continue
|
||||
return ''
|
||||
|
||||
|
||||
def _strip(s):
|
||||
return re.sub(r'<[^>]+>', '', s).strip().replace('\xa0', '').replace('\u00a0', '')
|
||||
|
||||
|
||||
def _to_float(s):
|
||||
try:
|
||||
return float(str(s).replace(' ', '').replace(',', ''))
|
||||
except:
|
||||
return None
|
||||
|
||||
|
||||
def _to_dt(s, fmt='%Y.%m.%d %H:%M:%S'):
|
||||
return pd.to_datetime(s, format=fmt, errors='coerce')
|
||||
|
||||
|
||||
def _enrich(df):
|
||||
"""Add derived columns common to all formats."""
|
||||
df['open_time'] = pd.to_datetime(df['open_time'], errors='coerce')
|
||||
df['close_time'] = pd.to_datetime(df['close_time'], errors='coerce')
|
||||
df['open_date'] = df['open_time'].dt.date
|
||||
df['close_date'] = df['close_time'].dt.date
|
||||
df['day_of_week'] = df['open_time'].dt.day_name()
|
||||
df['hour'] = df['open_time'].dt.hour
|
||||
df['duration_min'] = ((df['close_time'] - df['open_time'])
|
||||
.dt.total_seconds() / 60).round(1)
|
||||
for col in ['volume', 'open_price', 'close_price', 'sl', 'tp',
|
||||
'commission', 'swap', 'profit']:
|
||||
if col in df.columns:
|
||||
df[col] = pd.to_numeric(
|
||||
df[col].astype(str).str.replace(' ', '').str.replace(',', ''),
|
||||
errors='coerce'
|
||||
)
|
||||
if 'net_profit' not in df.columns:
|
||||
df['net_profit'] = (
|
||||
df.get('profit', 0).fillna(0) +
|
||||
df.get('commission', 0).fillna(0) +
|
||||
df.get('swap', 0).fillna(0)
|
||||
)
|
||||
df['win'] = df['net_profit'] > 0
|
||||
df['type'] = df['type'].str.lower().str.strip()
|
||||
df['strategy'] = df['comment'].apply(extract_strategy)
|
||||
# Normalise symbol — strip .a suffix for display matching
|
||||
df['symbol_base'] = df['symbol'].str.replace(r'\.[a-z]+$', '', regex=True).str.upper()
|
||||
return df
|
||||
|
||||
|
||||
# ── Format 1: Real Account HTM ────────────────────────────────────────────────
|
||||
|
||||
def parse_mt5_report(file_bytes):
|
||||
"""Parse MT5 real account HTML trade history report."""
|
||||
text = _decode(file_bytes)
|
||||
rows = re.findall(r'<tr[^>]*>(.*?)</tr>', text, re.DOTALL)
|
||||
trades = []
|
||||
in_trades = False
|
||||
|
||||
COLS = ['open_time', 'position', 'symbol', 'type', 'comment', 'volume',
|
||||
'open_price', 'sl', 'tp', 'close_time', 'close_price',
|
||||
'commission', 'swap', 'profit']
|
||||
|
||||
for row in rows:
|
||||
cells = re.findall(r'<t[dh][^>]*>(.*?)</t[dh]>', row, re.DOTALL)
|
||||
cells = [re.sub(r'\s+', ' ', _strip(c)).strip() for c in cells]
|
||||
|
||||
if cells and cells[0] == 'Time' and len(cells) >= 13:
|
||||
in_trades = True
|
||||
continue
|
||||
if not in_trades:
|
||||
continue
|
||||
if cells and any(kw in cells[0] for kw in
|
||||
['Total Net Profit', 'Results', 'Balance', 'Equity']):
|
||||
break
|
||||
|
||||
if len(cells) >= 14 and re.match(r'\d{4}\.\d{2}\.\d{2}', cells[0]):
|
||||
last = [c.lower() for c in cells if c]
|
||||
if any(s in last for s in ['placed', 'cancelled', 'expired', 'partial']):
|
||||
continue
|
||||
if len(cells) < 10 or not re.match(r'\d{4}\.\d{2}\.\d{2}', cells[9]):
|
||||
continue
|
||||
if '/' in str(cells[5]):
|
||||
continue
|
||||
try:
|
||||
trade = dict(zip(COLS, cells[:14]))
|
||||
trades.append(trade)
|
||||
except:
|
||||
continue
|
||||
|
||||
if not trades:
|
||||
return None
|
||||
|
||||
df = pd.DataFrame(trades)
|
||||
df['source'] = 'real'
|
||||
return _enrich(df)
|
||||
|
||||
|
||||
# ── Format 2: Backtest HTM ────────────────────────────────────────────────────
|
||||
|
||||
def parse_backtest_report(file_bytes):
|
||||
"""
|
||||
Parse MT5 Strategy Tester HTML report.
|
||||
Pairs in/out deals into complete trades.
|
||||
"""
|
||||
text = _decode(file_bytes)
|
||||
tables = re.findall(r'<table[^>]*>(.*?)</table>', text, re.DOTALL)
|
||||
if len(tables) < 2:
|
||||
return None
|
||||
|
||||
rows = re.findall(r'<tr[^>]*>(.*?)</tr>', tables[1], re.DOTALL)
|
||||
|
||||
# Find deals section
|
||||
in_deals = False
|
||||
deal_rows = []
|
||||
for row in rows:
|
||||
cells = [re.sub(r'\s+', ' ', _strip(c)).strip()
|
||||
for c in re.findall(r'<t[dh][^>]*>(.*?)</t[dh]>', row, re.DOTALL)]
|
||||
cells = [c for c in cells if c]
|
||||
if not cells:
|
||||
continue
|
||||
if 'Deals' in cells:
|
||||
in_deals = True
|
||||
continue
|
||||
if in_deals and cells[0] == 'Time' and 'Deal' in cells:
|
||||
continue # header row
|
||||
if in_deals and len(cells) >= 7 and re.match(r'\d{4}\.\d{2}\.\d{2}', cells[0]):
|
||||
deal_rows.append(cells)
|
||||
|
||||
if not deal_rows:
|
||||
return None
|
||||
|
||||
# Columns: Time, Deal, Symbol, Type, Direction, Volume, Price, Order,
|
||||
# Commission, Swap, Profit, Balance, Comment
|
||||
DEAL_COLS = ['time', 'deal', 'symbol', 'type', 'direction', 'volume',
|
||||
'price', 'order', 'commission', 'swap', 'profit', 'balance', 'comment']
|
||||
|
||||
deals = []
|
||||
for row in deal_rows:
|
||||
d = dict(zip(DEAL_COLS, row[:len(DEAL_COLS)]))
|
||||
deals.append(d)
|
||||
|
||||
df_deals = pd.DataFrame(deals)
|
||||
df_deals = df_deals[df_deals['direction'].isin(['in', 'out'])]
|
||||
|
||||
# Match in/out pairs — pair consecutive in→out by symbol+type
|
||||
open_stack = {} # key: (symbol, type_) -> list of open deals
|
||||
trades = []
|
||||
|
||||
for _, deal in df_deals.iterrows():
|
||||
sym = deal.get('symbol', '')
|
||||
typ = deal.get('type', '')
|
||||
dirn = deal.get('direction', '')
|
||||
key = (sym, typ)
|
||||
|
||||
if dirn == 'in':
|
||||
open_stack.setdefault(key, []).append(deal)
|
||||
elif dirn == 'out':
|
||||
stack = open_stack.get(key, [])
|
||||
if stack:
|
||||
entry = stack.pop(0)
|
||||
trades.append({
|
||||
'open_time' : entry['time'],
|
||||
'close_time' : deal['time'],
|
||||
'symbol' : sym,
|
||||
'type' : typ,
|
||||
'volume' : entry['volume'],
|
||||
'open_price' : entry['price'],
|
||||
'close_price': deal['price'],
|
||||
'sl' : None,
|
||||
'tp' : None,
|
||||
'commission' : _to_float(entry.get('commission', 0)),
|
||||
'swap' : _to_float(deal.get('swap', 0)),
|
||||
'profit' : _to_float(deal.get('profit', 0)),
|
||||
'comment' : deal.get('comment', ''),
|
||||
'position' : entry.get('deal', ''),
|
||||
})
|
||||
|
||||
if not trades:
|
||||
return None
|
||||
|
||||
df = pd.DataFrame(trades)
|
||||
df['source'] = 'backtest'
|
||||
return _enrich(df)
|
||||
|
||||
|
||||
# ── Format 3: Quant Analyzer CSV ─────────────────────────────────────────────
|
||||
|
||||
def parse_quant_csv(file_bytes):
|
||||
"""Parse Quant Analyzer listOfTrades CSV export."""
|
||||
try:
|
||||
text = file_bytes.decode('utf-8-sig')
|
||||
except:
|
||||
text = file_bytes.decode('latin-1')
|
||||
|
||||
from io import StringIO
|
||||
df_raw = pd.read_csv(StringIO(text))
|
||||
|
||||
# Normalise column names
|
||||
df_raw.columns = [c.strip().lower().replace(' ', '_').replace('/', '_').replace('(', '').replace(')', '') for c in df_raw.columns]
|
||||
|
||||
col_map = {
|
||||
'open_time' : ['open_time_$', 'open_time_$_', 'open_time', 'opentime'],
|
||||
'close_time' : ['close_time_$', 'close_time_$_', 'close_time', 'closetime'],
|
||||
'symbol' : ['symbol_$', 'symbol_$_', 'symbol'],
|
||||
'type' : ['type_$', 'type_$_', 'type', 'direction'],
|
||||
'volume' : ['size_$', 'size_$_', 'size', 'volume', 'lots'],
|
||||
'open_price' : ['open_price_$', 'open_price_$_', 'open_price', 'openprice'],
|
||||
'close_price' : ['close_price_$', 'close_price_$_', 'close_price', 'closeprice'],
|
||||
'profit' : ['profit_loss_$', 'profit_loss_$_', 'profit_loss', 'profit', 'net_profit'],
|
||||
'commission' : ['comm_swap_$', 'comm_swap_$_', 'commission', 'comm'],
|
||||
'swap' : ['swap_$', 'swap'],
|
||||
'sl' : ['stop_loss_$', 'stop_loss_$_', 'stop_loss', 'sl'],
|
||||
'comment' : ['comment_$', 'comment_$_', 'comment'],
|
||||
'strategy' : ['strategy_name_$', 'strategy_name_$_', 'strategy_name', 'strategy'],
|
||||
'mae' : ['mae_$', 'mae_$_', 'mae'],
|
||||
'mfe' : ['mfe_$', 'mfe_$_', 'mfe'],
|
||||
'drawdown' : ['drawdown_$', 'drawdown_$_', 'drawdown'],
|
||||
}
|
||||
|
||||
result = {}
|
||||
for target, candidates in col_map.items():
|
||||
for cand in candidates:
|
||||
if cand in df_raw.columns:
|
||||
result[target] = df_raw[cand]
|
||||
break
|
||||
|
||||
df = pd.DataFrame(result)
|
||||
|
||||
# Parse datetimes — QA uses DD.MM.YYYY HH:MM:SS
|
||||
for col in ['open_time', 'close_time']:
|
||||
if col in df.columns:
|
||||
df[col] = pd.to_datetime(df[col], format='%d.%m.%Y %H:%M:%S', errors='coerce')
|
||||
if df[col].isna().all():
|
||||
df[col] = pd.to_datetime(df[col], infer_datetime_format=True, errors='coerce')
|
||||
|
||||
# QA comm_swap is combined — split evenly as approximation if no separate swap
|
||||
if 'commission' in df.columns and 'swap' not in df.columns:
|
||||
df['swap'] = 0.0
|
||||
|
||||
if 'sl' not in df.columns:
|
||||
df['sl'] = None
|
||||
if 'tp' not in df.columns:
|
||||
df['tp'] = None
|
||||
if 'position' not in df.columns:
|
||||
df['position'] = df.get('ticket', range(len(df)))
|
||||
|
||||
df['source'] = 'quant_csv'
|
||||
|
||||
# Add extra QA-specific columns if present
|
||||
for extra in ['mae', 'mfe', 'drawdown']:
|
||||
if extra in df_raw.columns:
|
||||
df[extra] = pd.to_numeric(df_raw[extra], errors='coerce')
|
||||
|
||||
return _enrich(df)
|
||||
|
||||
|
||||
# ── Auto-detect format ────────────────────────────────────────────────────────
|
||||
|
||||
def detect_and_parse(file_bytes, filename=''):
|
||||
"""
|
||||
Auto-detect file format and parse.
|
||||
Returns (df, format_name) or (None, None).
|
||||
"""
|
||||
fname = filename.lower()
|
||||
|
||||
if fname.endswith('.csv'):
|
||||
df = parse_quant_csv(file_bytes)
|
||||
return df, 'Quant Analyzer CSV'
|
||||
|
||||
# HTML/HTM — detect backtest vs real account
|
||||
try:
|
||||
text = _decode(file_bytes)
|
||||
except:
|
||||
return None, None
|
||||
|
||||
if 'Strategy Tester Report' in text or 'strategy tester' in text.lower():
|
||||
df = parse_backtest_report(file_bytes)
|
||||
return df, 'MT5 Backtest Report'
|
||||
|
||||
df = parse_mt5_report(file_bytes)
|
||||
return df, 'MT5 Account History'
|
||||
|
||||
|
||||
# ── Stats ─────────────────────────────────────────────────────────────────────
|
||||
|
||||
def calc_stats(df):
|
||||
if df is None or len(df) == 0:
|
||||
return {}
|
||||
|
||||
total = len(df)
|
||||
wins = df[df['win'] == True]
|
||||
losses = df[df['win'] == False]
|
||||
win_rate = round(len(wins) / total * 100, 1) if total > 0 else 0
|
||||
gross_profit = round(wins['net_profit'].sum(), 2)
|
||||
gross_loss = round(losses['net_profit'].sum(), 2)
|
||||
net_profit = round(df['net_profit'].sum(), 2)
|
||||
pf = round(abs(gross_profit / gross_loss), 2) if gross_loss != 0 else float('inf')
|
||||
avg_win = round(wins['net_profit'].mean(), 2) if len(wins) > 0 else 0
|
||||
avg_loss = round(losses['net_profit'].mean(), 2) if len(losses) > 0 else 0
|
||||
rr = round(abs(avg_win / avg_loss), 2) if avg_loss != 0 else float('inf')
|
||||
expectancy = round((win_rate/100 * avg_win) + ((1 - win_rate/100) * avg_loss), 2)
|
||||
|
||||
results = df.sort_values('close_time')['win'].tolist()
|
||||
max_cw = _max_consec(results, True)
|
||||
max_cl = _max_consec(results, False)
|
||||
|
||||
cumulative = df.sort_values('close_time')['net_profit'].cumsum()
|
||||
rolling_max = cumulative.cummax()
|
||||
max_dd = round((cumulative - rolling_max).min(), 2)
|
||||
|
||||
avg_dur = round(df['duration_min'].mean(), 1) if 'duration_min' in df.columns else 0
|
||||
avg_win_dur = round(wins['duration_min'].mean(), 1) if len(wins) > 0 else 0
|
||||
avg_los_dur = round(losses['duration_min'].mean(), 1) if len(losses) > 0 else 0
|
||||
|
||||
longs = df[df['type'] == 'buy']
|
||||
shorts = df[df['type'] == 'sell']
|
||||
|
||||
return {
|
||||
'total_trades' : total,
|
||||
'win_rate' : win_rate,
|
||||
'net_profit' : net_profit,
|
||||
'gross_profit' : gross_profit,
|
||||
'gross_loss' : gross_loss,
|
||||
'profit_factor' : pf,
|
||||
'avg_win' : avg_win,
|
||||
'avg_loss' : avg_loss,
|
||||
'rr_ratio' : rr,
|
||||
'expectancy' : expectancy,
|
||||
'max_consec_wins' : max_cw,
|
||||
'max_consec_losses' : max_cl,
|
||||
'max_drawdown' : max_dd,
|
||||
'best_trade' : round(df['net_profit'].max(), 2),
|
||||
'worst_trade' : round(df['net_profit'].min(), 2),
|
||||
'avg_duration_min' : avg_dur,
|
||||
'avg_win_duration' : avg_win_dur,
|
||||
'avg_loss_duration' : avg_los_dur,
|
||||
'long_trades' : len(longs),
|
||||
'short_trades' : len(shorts),
|
||||
'long_win_rate' : round(len(longs[longs['win']]) / len(longs) * 100, 1) if len(longs) > 0 else 0,
|
||||
'short_win_rate' : round(len(shorts[shorts['win']]) / len(shorts) * 100, 1) if len(shorts) > 0 else 0,
|
||||
}
|
||||
|
||||
|
||||
def extract_strategy(comment):
|
||||
if not comment or str(comment).strip() == '':
|
||||
return 'Manual'
|
||||
parts = str(comment).split('_')
|
||||
while parts and re.match(r'^\d+$', parts[-1]):
|
||||
parts.pop()
|
||||
if parts and re.match(r'^[A-Z]{3,8}(\.a)?$', parts[-1]):
|
||||
parts.pop()
|
||||
return '_'.join(parts) if parts else str(comment)
|
||||
|
||||
|
||||
def _max_consec(results, target):
|
||||
max_c = cur_c = 0
|
||||
for r in results:
|
||||
if r == target:
|
||||
cur_c += 1
|
||||
max_c = max(max_c, cur_c)
|
||||
else:
|
||||
cur_c = 0
|
||||
return max_c
|
||||
@@ -0,0 +1,4 @@
|
||||
streamlit
|
||||
streamlit-option-menu
|
||||
pandas
|
||||
plotly
|
||||
@@ -0,0 +1,94 @@
|
||||
import io
|
||||
import zipfile
|
||||
from pathlib import Path
|
||||
|
||||
def parse_set_file(file_bytes, filename):
|
||||
"""Parse a .set file and return dict of {param: value} and ordered param list"""
|
||||
text = file_bytes.decode('utf-16')
|
||||
params = {}
|
||||
raw_lines = {} # preserve full line for export
|
||||
order = []
|
||||
|
||||
for line in text.splitlines():
|
||||
line = line.strip()
|
||||
if not line or line.startswith(';'):
|
||||
continue
|
||||
if '=' not in line:
|
||||
continue
|
||||
key, _, rest = line.partition('=')
|
||||
key = key.strip()
|
||||
# Value is first field before ||
|
||||
parts = rest.split('||')
|
||||
value = parts[0].strip()
|
||||
params[key] = value
|
||||
raw_lines[key] = rest # preserve everything after =
|
||||
order.append(key)
|
||||
|
||||
return params, raw_lines, order
|
||||
|
||||
def build_comparison_df(files_data):
|
||||
"""
|
||||
files_data: list of (filename, params, raw_lines, order)
|
||||
Returns DataFrame with param names as index, filenames as columns
|
||||
"""
|
||||
import pandas as pd
|
||||
|
||||
# Build union of all param keys preserving order from first file
|
||||
all_keys = []
|
||||
seen = set()
|
||||
for _, _, _, order in files_data:
|
||||
for k in order:
|
||||
if k not in seen:
|
||||
all_keys.append(k)
|
||||
seen.add(k)
|
||||
|
||||
# Build dataframe
|
||||
rows = []
|
||||
for key in all_keys:
|
||||
row = {'Parameter': key}
|
||||
for filename, params, _, _ in files_data:
|
||||
row[filename] = params.get(key, '')
|
||||
rows.append(row)
|
||||
|
||||
return pd.DataFrame(rows)
|
||||
|
||||
def export_set_file(filename, params_edited, raw_lines, order, original_bytes):
|
||||
"""
|
||||
Rebuild .set file with edited values, preserving || fields
|
||||
Returns bytes (utf-16 encoded)
|
||||
"""
|
||||
# Get original header comments
|
||||
text = original_bytes.decode('utf-16')
|
||||
lines = text.splitlines()
|
||||
header_lines = []
|
||||
for line in lines:
|
||||
if line.startswith(';'):
|
||||
header_lines.append(line)
|
||||
else:
|
||||
break
|
||||
|
||||
output_lines = header_lines.copy()
|
||||
|
||||
for key in order:
|
||||
if key not in params_edited:
|
||||
continue
|
||||
new_value = params_edited[key]
|
||||
rest = raw_lines.get(key, new_value)
|
||||
parts = rest.split('||')
|
||||
parts[0] = str(new_value)
|
||||
output_lines.append(f"{key}={'||'.join(parts)}")
|
||||
|
||||
content = '\r\n'.join(output_lines) + '\r\n'
|
||||
return content.encode('utf-16')
|
||||
|
||||
def create_zip(files_export):
|
||||
"""
|
||||
files_export: list of (filename, bytes)
|
||||
Returns zip bytes
|
||||
"""
|
||||
buf = io.BytesIO()
|
||||
with zipfile.ZipFile(buf, 'w', zipfile.ZIP_DEFLATED) as zf:
|
||||
for fname, fbytes in files_export:
|
||||
zf.writestr(fname, fbytes)
|
||||
buf.seek(0)
|
||||
return buf.read()
|
||||
+37
@@ -0,0 +1,37 @@
|
||||
"""
|
||||
pages/settings.py
|
||||
=================
|
||||
Settings page for MT5 Tools dashboard.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
|
||||
|
||||
def render():
|
||||
st.title("⚙️ Settings")
|
||||
|
||||
st.markdown("""
|
||||
<div class="info-card">
|
||||
MT5 Tools — settings and information.
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
st.subheader("About")
|
||||
st.markdown("""
|
||||
**MT5 Tools** is a standalone trade analysis and comparison dashboard.
|
||||
|
||||
**Supported file formats:**
|
||||
- MT5 Account History HTML export (`.htm` / `.html`)
|
||||
- MT5 Strategy Tester Backtest Report (`.htm` / `.html`)
|
||||
- Quant Analyzer CSV export (`listOfTrades_*.csv`)
|
||||
|
||||
**Pages:**
|
||||
- **Trade Analysis** — statistics, equity curves, day/hour breakdown for a single report
|
||||
- **Trade Compare** — match and compare two reports to measure slippage and variance
|
||||
""")
|
||||
|
||||
st.subheader("Requirements")
|
||||
st.code("pip install streamlit streamlit-option-menu pandas plotly", language="bash")
|
||||
|
||||
st.subheader("Launch")
|
||||
st.code("streamlit run app.py", language="bash")
|
||||
@@ -0,0 +1,333 @@
|
||||
"""
|
||||
pages/trade_analysis.py
|
||||
=======================
|
||||
MT5 Trade Analysis page — migrated from main dashboard.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import plotly.graph_objects as go
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
from mt5_parser import detect_and_parse, calc_stats
|
||||
|
||||
|
||||
def render():
|
||||
st.title("📊 Trade Analysis")
|
||||
|
||||
# ── Session state ─────────────────────────────────────────────────────────
|
||||
if 'ta_df' not in st.session_state:
|
||||
st.session_state['ta_df'] = None
|
||||
st.session_state['ta_format'] = None
|
||||
|
||||
# ── File upload ───────────────────────────────────────────────────────────
|
||||
col1, col2 = st.columns([4, 1])
|
||||
with col1:
|
||||
uploaded = st.file_uploader(
|
||||
"Upload MT5 Report (HTM/HTML) or Quant Analyzer CSV",
|
||||
type=['html', 'htm', 'csv'],
|
||||
key='ta_upload'
|
||||
)
|
||||
with col2:
|
||||
st.markdown("<br>", unsafe_allow_html=True)
|
||||
if st.button("🗑 Clear", key='ta_clear'):
|
||||
st.session_state['ta_df'] = None
|
||||
st.session_state['ta_format'] = None
|
||||
st.rerun()
|
||||
|
||||
if uploaded:
|
||||
df, fmt = detect_and_parse(uploaded.read(), uploaded.name)
|
||||
if df is not None:
|
||||
st.session_state['ta_df'] = df
|
||||
st.session_state['ta_format'] = fmt
|
||||
st.success(f"✓ Loaded {len(df)} trades — {fmt}")
|
||||
else:
|
||||
st.error("Could not parse report — check file format")
|
||||
|
||||
df_all = st.session_state['ta_df']
|
||||
fmt = st.session_state['ta_format']
|
||||
|
||||
if df_all is None or len(df_all) == 0:
|
||||
st.markdown("""
|
||||
<div class="info-card">
|
||||
Upload an MT5 account history report (.htm/.html), MT5 backtest report,
|
||||
or a Quant Analyzer CSV export to begin analysis.
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
return
|
||||
|
||||
if fmt:
|
||||
st.caption(f"Format detected: **{fmt}** · {len(df_all)} total trades")
|
||||
|
||||
# ── Filters ───────────────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
fc1, fc2, fc3, fc4 = st.columns(4)
|
||||
|
||||
with fc1:
|
||||
date_min = df_all['open_time'].min().date()
|
||||
date_max = df_all['open_time'].max().date()
|
||||
date_from = st.date_input("From", value=date_min, min_value=date_min,
|
||||
max_value=date_max, key='ta_from')
|
||||
date_to = st.date_input("To", value=date_max, min_value=date_min,
|
||||
max_value=date_max, key='ta_to')
|
||||
|
||||
with fc2:
|
||||
symbols = sorted(df_all['symbol'].dropna().unique().tolist())
|
||||
sel_symbol = st.multiselect("Symbol", symbols, key='ta_sym')
|
||||
|
||||
with fc3:
|
||||
strategies = sorted(df_all['strategy'].dropna().unique().tolist())
|
||||
sel_strategy = st.multiselect("Strategy / EA", strategies, key='ta_strat')
|
||||
|
||||
with fc4:
|
||||
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
|
||||
sel_days = st.multiselect("Day of week", days, key='ta_days')
|
||||
sel_type = st.multiselect("Type", ['buy', 'sell'], key='ta_type')
|
||||
|
||||
# Apply filters
|
||||
df = df_all.copy()
|
||||
df = df[(df['open_time'].dt.date >= date_from) &
|
||||
(df['open_time'].dt.date <= date_to)]
|
||||
if sel_symbol:
|
||||
df = df[df['symbol'].isin(sel_symbol)]
|
||||
if sel_strategy:
|
||||
df = df[df['strategy'].isin(sel_strategy)]
|
||||
if sel_days:
|
||||
df = df[df['day_of_week'].isin(sel_days)]
|
||||
if sel_type:
|
||||
df = df[df['type'].isin(sel_type)]
|
||||
|
||||
st.caption(f"Showing **{len(df)}** trades after filters")
|
||||
|
||||
# ── Analysis mode ─────────────────────────────────────────────────────────
|
||||
mode = st.radio(
|
||||
"Analysis mode",
|
||||
["Overall", "By Strategy", "By Symbol", "By Day of Week"],
|
||||
horizontal=True, key='ta_mode'
|
||||
)
|
||||
st.divider()
|
||||
|
||||
# ── Helpers ───────────────────────────────────────────────────────────────
|
||||
def render_stats(stats, label=""):
|
||||
if label:
|
||||
st.markdown(f"**{label}**")
|
||||
|
||||
c1, c2, c3, c4, c5 = st.columns(5)
|
||||
c1.metric("Net Profit", f"${stats['net_profit']:,.2f}")
|
||||
c2.metric("Win Rate", f"{stats['win_rate']}%")
|
||||
c3.metric("Profit Factor", f"{stats['profit_factor']}")
|
||||
c4.metric("R:R Ratio", f"{stats['rr_ratio']}")
|
||||
c5.metric("Expectancy", f"${stats['expectancy']:,.2f}")
|
||||
|
||||
c1, c2, c3, c4, c5 = st.columns(5)
|
||||
c1.metric("Total Trades", stats['total_trades'])
|
||||
c2.metric("Avg Win", f"${stats['avg_win']:,.2f}")
|
||||
c3.metric("Avg Loss", f"${stats['avg_loss']:,.2f}")
|
||||
c4.metric("Max DD", f"${stats['max_drawdown']:,.2f}")
|
||||
c5.metric("Best Trade", f"${stats['best_trade']:,.2f}")
|
||||
|
||||
c1, c2, c3, c4, c5 = st.columns(5)
|
||||
c1.metric("Max Consec Wins", stats['max_consec_wins'])
|
||||
c2.metric("Max Consec Losses", stats['max_consec_losses'])
|
||||
c3.metric("Avg Win Dur", f"{stats['avg_win_duration']}m")
|
||||
c4.metric("Avg Loss Dur", f"{stats['avg_loss_duration']}m")
|
||||
c5.metric("Worst Trade", f"${stats['worst_trade']:,.2f}")
|
||||
|
||||
c1, c2, c3, c4 = st.columns(4)
|
||||
c1.metric("Long Trades", stats['long_trades'])
|
||||
c2.metric("Long Win Rate", f"{stats['long_win_rate']}%")
|
||||
c3.metric("Short Trades", stats['short_trades'])
|
||||
c4.metric("Short Win Rate",f"{stats['short_win_rate']}%")
|
||||
|
||||
def render_equity_curve(df_plot, label="Equity Curve"):
|
||||
df_s = df_plot.sort_values('close_time').copy()
|
||||
df_s['cumulative'] = df_s['net_profit'].cumsum()
|
||||
fig = go.Figure()
|
||||
fig.add_trace(go.Scatter(
|
||||
x=df_s['close_time'], y=df_s['cumulative'],
|
||||
mode='lines',
|
||||
line=dict(color='#7c6af7', width=2),
|
||||
fill='tozeroy',
|
||||
fillcolor='rgba(124,106,247,0.08)',
|
||||
name='Equity'
|
||||
))
|
||||
fig.update_layout(
|
||||
title=label, height=300,
|
||||
plot_bgcolor='rgba(10,10,15,1)',
|
||||
paper_bgcolor='rgba(10,10,15,1)',
|
||||
font=dict(color='#aaa', family='JetBrains Mono'),
|
||||
xaxis=dict(gridcolor='rgba(255,255,255,0.04)'),
|
||||
yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
|
||||
margin=dict(l=60, r=20, t=40, b=40)
|
||||
)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
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(10,10,15,1)', paper_bgcolor='rgba(10,10,15,1)',
|
||||
font=dict(color='#aaa'), margin=dict(l=60, r=20, t=40, b=40),
|
||||
xaxis=dict(gridcolor='rgba(255,255,255,0.04)'),
|
||||
yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
|
||||
legend=dict(bgcolor='rgba(0,0,0,0.3)')
|
||||
)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
dt = dow.reset_index()
|
||||
dt.columns = ['Day', 'Trades', 'Net Profit', 'Win Rate %']
|
||||
dt['Net Profit'] = dt['Net Profit'].round(2)
|
||||
st.dataframe(dt, use_container_width=True, hide_index=True)
|
||||
|
||||
def render_hour_chart(df_plot):
|
||||
hourly = df_plot.groupby('hour').agg(
|
||||
trades = ('net_profit', 'count'),
|
||||
net_profit = ('net_profit', 'sum'),
|
||||
)
|
||||
wins_h = df_plot[df_plot['win']].groupby('hour')['net_profit'].sum().reindex(hourly.index, fill_value=0)
|
||||
losses_h = df_plot[~df_plot['win']].groupby('hour')['net_profit'].sum().reindex(hourly.index, fill_value=0)
|
||||
|
||||
fig = go.Figure()
|
||||
fig.add_trace(go.Bar(x=wins_h.index, y=wins_h, name='Profit', marker_color='rgba(45,198,83,0.8)'))
|
||||
fig.add_trace(go.Bar(x=losses_h.index, y=losses_h, name='Loss', marker_color='rgba(230,57,70,0.8)'))
|
||||
fig.update_layout(
|
||||
title='P&L by Hour of Day', height=280, barmode='relative',
|
||||
plot_bgcolor='rgba(10,10,15,1)', paper_bgcolor='rgba(10,10,15,1)',
|
||||
font=dict(color='#aaa'), margin=dict(l=60, r=20, t=40, b=40),
|
||||
xaxis=dict(gridcolor='rgba(255,255,255,0.04)', title='Hour (UTC)'),
|
||||
yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
|
||||
legend=dict(bgcolor='rgba(0,0,0,0.3)')
|
||||
)
|
||||
st.plotly_chart(fig, use_container_width=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)
|
||||
render_stats(stats, "Overall Statistics")
|
||||
render_equity_curve(df)
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
render_dow_chart(df)
|
||||
with col2:
|
||||
render_hour_chart(df)
|
||||
|
||||
elif mode == "By Strategy":
|
||||
strats = sorted(df['strategy'].dropna().unique().tolist())
|
||||
if not strats:
|
||||
st.info("No strategies found")
|
||||
else:
|
||||
st.subheader("Strategy Comparison")
|
||||
rows = []
|
||||
for s in strats:
|
||||
sdf = df[df['strategy'] == s]
|
||||
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'
|
||||
)
|
||||
@@ -0,0 +1,422 @@
|
||||
"""
|
||||
pages/trade_compare.py
|
||||
======================
|
||||
Side-by-side comparison of two trade history files.
|
||||
Matches trades by symbol + type + open time within a tolerance window.
|
||||
Highlights slippage, profit variance, and timing differences.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
from mt5_parser import detect_and_parse, calc_stats
|
||||
|
||||
|
||||
# ── Match trades ──────────────────────────────────────────────────────────────
|
||||
|
||||
def match_trades(df_a, df_b, tolerance_hours):
|
||||
"""
|
||||
Match trades between two DataFrames.
|
||||
Match criteria: same symbol_base + same type + open_time within tolerance.
|
||||
Returns DataFrame of matched pairs with diff columns.
|
||||
"""
|
||||
tol = pd.Timedelta(hours=tolerance_hours)
|
||||
matched = []
|
||||
used_b = set()
|
||||
|
||||
for i, a in df_a.iterrows():
|
||||
best_match = None
|
||||
best_delta = tol + pd.Timedelta(seconds=1)
|
||||
|
||||
for j, b in df_b.iterrows():
|
||||
if j in used_b:
|
||||
continue
|
||||
if a['symbol_base'] != b['symbol_base']:
|
||||
continue
|
||||
if a['type'] != b['type']:
|
||||
continue
|
||||
delta = abs(a['open_time'] - b['open_time'])
|
||||
if delta <= tol and delta < best_delta:
|
||||
best_delta = delta
|
||||
best_match = (j, b)
|
||||
|
||||
if best_match:
|
||||
j, b = best_match
|
||||
used_b.add(j)
|
||||
|
||||
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
|
||||
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
|
||||
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
|
||||
time_diff = round((b['open_time'] - a['open_time']).total_seconds() / 60, 1)
|
||||
dur_diff = round(float(b.get('duration_min', 0) or 0) - float(a.get('duration_min', 0) or 0), 1)
|
||||
|
||||
matched.append({
|
||||
# File A
|
||||
'A_open_time' : a['open_time'],
|
||||
'A_close_time' : a['close_time'],
|
||||
'A_symbol' : a['symbol'],
|
||||
'A_type' : a['type'],
|
||||
'A_volume' : a.get('volume'),
|
||||
'A_open_price' : a.get('open_price'),
|
||||
'A_close_price': a.get('close_price'),
|
||||
'A_profit' : a.get('net_profit'),
|
||||
'A_duration' : a.get('duration_min'),
|
||||
# File B
|
||||
'B_open_time' : b['open_time'],
|
||||
'B_close_time' : b['close_time'],
|
||||
'B_symbol' : b['symbol'],
|
||||
'B_type' : b['type'],
|
||||
'B_volume' : b.get('volume'),
|
||||
'B_open_price' : b.get('open_price'),
|
||||
'B_close_price': b.get('close_price'),
|
||||
'B_profit' : b.get('net_profit'),
|
||||
'B_duration' : b.get('duration_min'),
|
||||
# Differences
|
||||
'open_slippage' : open_slip,
|
||||
'close_slippage': close_slip,
|
||||
'profit_var' : profit_var,
|
||||
'time_diff_min' : time_diff,
|
||||
'duration_diff' : dur_diff,
|
||||
})
|
||||
|
||||
return pd.DataFrame(matched)
|
||||
|
||||
|
||||
# ── Render ────────────────────────────────────────────────────────────────────
|
||||
|
||||
def render():
|
||||
st.title("🔄 Trade Compare")
|
||||
|
||||
st.markdown("""
|
||||
<div class="info-card">
|
||||
Compare two trade history files — backtest vs real account, or any two exports.
|
||||
Trades are matched by symbol, direction, and open time within a configurable
|
||||
tolerance window to account for gaps, slippage, and market open variations.
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# ── Session state ─────────────────────────────────────────────────────────
|
||||
for k in ['tc_df_a', 'tc_df_b', 'tc_fmt_a', 'tc_fmt_b']:
|
||||
if k not in st.session_state:
|
||||
st.session_state[k] = None
|
||||
|
||||
# ── File upload ───────────────────────────────────────────────────────────
|
||||
st.subheader("Load Files")
|
||||
col_a, col_b = st.columns(2)
|
||||
|
||||
with col_a:
|
||||
st.markdown("**File A** — Reference (e.g. Backtest)")
|
||||
up_a = st.file_uploader("Upload File A", type=['html','htm','csv'], key='tc_up_a')
|
||||
if up_a:
|
||||
df_a, fmt_a = detect_and_parse(up_a.read(), up_a.name)
|
||||
if df_a is not None:
|
||||
st.session_state['tc_df_a'] = df_a
|
||||
st.session_state['tc_fmt_a'] = fmt_a
|
||||
st.success(f"✓ {len(df_a)} trades — {fmt_a}")
|
||||
else:
|
||||
st.error("Could not parse File A")
|
||||
if st.session_state['tc_df_a'] is not None:
|
||||
st.caption(f"Loaded: **{st.session_state['tc_fmt_a']}** · {len(st.session_state['tc_df_a'])} trades")
|
||||
|
||||
with col_b:
|
||||
st.markdown("**File B** — Comparison (e.g. Real Account)")
|
||||
up_b = st.file_uploader("Upload File B", type=['html','htm','csv'], key='tc_up_b')
|
||||
if up_b:
|
||||
df_b, fmt_b = detect_and_parse(up_b.read(), up_b.name)
|
||||
if df_b is not None:
|
||||
st.session_state['tc_df_b'] = df_b
|
||||
st.session_state['tc_fmt_b'] = fmt_b
|
||||
st.success(f"✓ {len(df_b)} trades — {fmt_b}")
|
||||
else:
|
||||
st.error("Could not parse File B")
|
||||
if st.session_state['tc_df_b'] is not None:
|
||||
st.caption(f"Loaded: **{st.session_state['tc_fmt_b']}** · {len(st.session_state['tc_df_b'])} trades")
|
||||
|
||||
df_a = st.session_state['tc_df_a']
|
||||
df_b = st.session_state['tc_df_b']
|
||||
|
||||
if df_a is None or df_b is None:
|
||||
return
|
||||
|
||||
# ── Filters ───────────────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
st.subheader("Filters")
|
||||
|
||||
fa1, fa2, fa3 = st.columns(3)
|
||||
fb1, fb2, fb3 = st.columns(3)
|
||||
|
||||
with fa1:
|
||||
st.markdown("**File A filters**")
|
||||
with fb1:
|
||||
st.markdown("**File B filters**")
|
||||
|
||||
col1, col2, col3, col4, col5, col6 = st.columns(6)
|
||||
|
||||
with col1:
|
||||
a_date_min = df_a['open_time'].min().date()
|
||||
a_date_max = df_a['open_time'].max().date()
|
||||
a_from = st.date_input("A — From", value=a_date_min, min_value=a_date_min,
|
||||
max_value=a_date_max, key='tc_a_from')
|
||||
a_to = st.date_input("A — To", value=a_date_max, min_value=a_date_min,
|
||||
max_value=a_date_max, key='tc_a_to')
|
||||
|
||||
with col2:
|
||||
a_syms = sorted(df_a['symbol'].dropna().unique().tolist())
|
||||
a_sel_sym = st.multiselect("A — Symbol", a_syms, key='tc_a_sym')
|
||||
|
||||
with col3:
|
||||
a_strats = sorted(df_a['strategy'].dropna().unique().tolist())
|
||||
a_sel_strat = st.multiselect("A — Strategy", a_strats, key='tc_a_strat')
|
||||
a_sel_type = st.multiselect("A — Type", ['buy', 'sell'], key='tc_a_type')
|
||||
|
||||
with col4:
|
||||
b_date_min = df_b['open_time'].min().date()
|
||||
b_date_max = df_b['open_time'].max().date()
|
||||
b_from = st.date_input("B — From", value=b_date_min, min_value=b_date_min,
|
||||
max_value=b_date_max, key='tc_b_from')
|
||||
b_to = st.date_input("B — To", value=b_date_max, min_value=b_date_min,
|
||||
max_value=b_date_max, key='tc_b_to')
|
||||
|
||||
with col5:
|
||||
b_syms = sorted(df_b['symbol'].dropna().unique().tolist())
|
||||
b_sel_sym = st.multiselect("B — Symbol", b_syms, key='tc_b_sym')
|
||||
|
||||
with col6:
|
||||
b_strats = sorted(df_b['strategy'].dropna().unique().tolist())
|
||||
b_sel_strat = st.multiselect("B — Strategy", b_strats, key='tc_b_strat')
|
||||
b_sel_type = st.multiselect("B — Type", ['buy', 'sell'], key='tc_b_type')
|
||||
|
||||
# ── Matching tolerance ────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
col_tol, col_run = st.columns([3, 1])
|
||||
with col_tol:
|
||||
tolerance = st.slider(
|
||||
"Match tolerance (hours) — max time difference between A and B open times",
|
||||
min_value=1, max_value=24, value=4, step=1,
|
||||
help="Trades within this window are considered the same setup. "
|
||||
"Increase for daily charts, decrease for intraday."
|
||||
)
|
||||
with col_run:
|
||||
st.markdown("<br>", unsafe_allow_html=True)
|
||||
run = st.button("🔍 Match Trades", type="primary", use_container_width=True)
|
||||
|
||||
if not run and 'tc_matched' not in st.session_state:
|
||||
return
|
||||
|
||||
# Apply filters
|
||||
fa = df_a.copy()
|
||||
fa = fa[(fa['open_time'].dt.date >= a_from) & (fa['open_time'].dt.date <= a_to)]
|
||||
if a_sel_sym: fa = fa[fa['symbol'].isin(a_sel_sym)]
|
||||
if a_sel_strat: fa = fa[fa['strategy'].isin(a_sel_strat)]
|
||||
if a_sel_type: fa = fa[fa['type'].isin(a_sel_type)]
|
||||
|
||||
fb = df_b.copy()
|
||||
fb = fb[(fb['open_time'].dt.date >= b_from) & (fb['open_time'].dt.date <= b_to)]
|
||||
if b_sel_sym: fb = fb[fb['symbol'].isin(b_sel_sym)]
|
||||
if b_sel_strat: fb = fb[fb['strategy'].isin(b_sel_strat)]
|
||||
if b_sel_type: fb = fb[fb['type'].isin(b_sel_type)]
|
||||
|
||||
if run:
|
||||
with st.spinner("Matching trades..."):
|
||||
matched = match_trades(fa, fb, tolerance)
|
||||
st.session_state['tc_matched'] = matched
|
||||
st.session_state['tc_fa_len'] = len(fa)
|
||||
st.session_state['tc_fb_len'] = len(fb)
|
||||
|
||||
matched = st.session_state.get('tc_matched', pd.DataFrame())
|
||||
fa_len = st.session_state.get('tc_fa_len', len(fa))
|
||||
fb_len = st.session_state.get('tc_fb_len', len(fb))
|
||||
|
||||
if matched is None or len(matched) == 0:
|
||||
st.warning("No matching trades found — try increasing the tolerance window or adjusting filters.")
|
||||
return
|
||||
|
||||
# ── Summary stats ─────────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
st.subheader("Match Summary")
|
||||
|
||||
m1, m2, m3, m4, m5 = st.columns(5)
|
||||
m1.metric("File A Trades", fa_len)
|
||||
m2.metric("File B Trades", fb_len)
|
||||
m3.metric("Matched Pairs", len(matched))
|
||||
m4.metric("Unmatched A", fa_len - len(matched))
|
||||
m5.metric("Unmatched B", fb_len - len(matched))
|
||||
|
||||
st.divider()
|
||||
|
||||
# ── Aggregate comparison ───────────────────────────────────────────────────
|
||||
st.subheader("Aggregate Comparison")
|
||||
|
||||
ac1, ac2 = st.columns(2)
|
||||
|
||||
with ac1:
|
||||
st.markdown("**File A (Reference)**")
|
||||
a_net = matched['A_profit'].sum()
|
||||
a_wr = (matched['A_profit'] > 0).mean() * 100
|
||||
a_avg = matched['A_profit'].mean()
|
||||
a_dur = matched['A_duration'].mean() if 'A_duration' in matched else None
|
||||
st.metric("Net Profit", f"${a_net:,.2f}")
|
||||
st.metric("Win Rate", f"{a_wr:.1f}%")
|
||||
st.metric("Avg Profit", f"${a_avg:,.2f}")
|
||||
if a_dur:
|
||||
st.metric("Avg Duration", f"{a_dur:.0f}m")
|
||||
|
||||
with ac2:
|
||||
st.markdown("**File B (Comparison)**")
|
||||
b_net = matched['B_profit'].sum()
|
||||
b_wr = (matched['B_profit'] > 0).mean() * 100
|
||||
b_avg = matched['B_profit'].mean()
|
||||
b_dur = matched['B_duration'].mean() if 'B_duration' in matched else None
|
||||
delta_net = b_net - a_net
|
||||
st.metric("Net Profit", f"${b_net:,.2f}",
|
||||
delta=f"{delta_net:+.2f}", delta_color="normal")
|
||||
st.metric("Win Rate", f"{b_wr:.1f}%",
|
||||
delta=f"{b_wr - a_wr:+.1f}%", delta_color="normal")
|
||||
st.metric("Avg Profit", f"${b_avg:,.2f}",
|
||||
delta=f"{b_avg - a_avg:+.2f}", delta_color="normal")
|
||||
if b_dur and a_dur:
|
||||
st.metric("Avg Duration", f"{b_dur:.0f}m",
|
||||
delta=f"{b_dur - a_dur:+.0f}m", delta_color="off")
|
||||
|
||||
# ── Slippage summary ───────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
st.subheader("Slippage & Variance Summary")
|
||||
|
||||
sc1, sc2, sc3, sc4 = st.columns(4)
|
||||
avg_open_slip = matched['open_slippage'].mean()
|
||||
avg_close_slip = matched['close_slip'].mean() if 'close_slip' in matched else matched['close_slippage'].mean()
|
||||
avg_profit_var = matched['profit_var'].mean()
|
||||
avg_time_diff = matched['time_diff_min'].mean()
|
||||
|
||||
sc1.metric("Avg Entry Slippage", f"{avg_open_slip:+.5f}" if pd.notna(avg_open_slip) else "N/A",
|
||||
help="B open price minus A open price. Positive = B filled higher.")
|
||||
sc2.metric("Avg Exit Slippage", f"{avg_close_slip:+.5f}" if pd.notna(avg_close_slip) else "N/A",
|
||||
help="B close price minus A close price.")
|
||||
sc3.metric("Avg Profit Variance", f"${avg_profit_var:+.2f}" if pd.notna(avg_profit_var) else "N/A",
|
||||
help="B net profit minus A net profit per trade.")
|
||||
sc4.metric("Avg Time Difference", f"{avg_time_diff:+.0f}m" if pd.notna(avg_time_diff) else "N/A",
|
||||
help="B open time minus A open time in minutes.")
|
||||
|
||||
# ── Equity curve overlay ───────────────────────────────────────────────────
|
||||
st.divider()
|
||||
st.subheader("Equity Curve Overlay")
|
||||
|
||||
m_sorted = matched.sort_values('A_open_time')
|
||||
fig = go.Figure()
|
||||
fig.add_trace(go.Scatter(
|
||||
x=m_sorted['A_open_time'],
|
||||
y=m_sorted['A_profit'].cumsum(),
|
||||
mode='lines', name='File A',
|
||||
line=dict(color='#7c6af7', width=2),
|
||||
fill='tozeroy', fillcolor='rgba(124,106,247,0.05)'
|
||||
))
|
||||
fig.add_trace(go.Scatter(
|
||||
x=m_sorted['B_open_time'],
|
||||
y=m_sorted['B_profit'].cumsum(),
|
||||
mode='lines', name='File B',
|
||||
line=dict(color='#2dc653', width=2),
|
||||
fill='tozeroy', fillcolor='rgba(45,198,83,0.05)'
|
||||
))
|
||||
fig.update_layout(
|
||||
height=320,
|
||||
plot_bgcolor='rgba(10,10,15,1)',
|
||||
paper_bgcolor='rgba(10,10,15,1)',
|
||||
font=dict(color='#aaa', family='JetBrains Mono'),
|
||||
xaxis=dict(gridcolor='rgba(255,255,255,0.04)'),
|
||||
yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
|
||||
legend=dict(bgcolor='rgba(0,0,0,0.3)'),
|
||||
margin=dict(l=60, r=20, t=20, b=40)
|
||||
)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
# ── Profit variance scatter ────────────────────────────────────────────────
|
||||
st.subheader("Profit Variance per Trade")
|
||||
fig2 = go.Figure()
|
||||
colours = matched['profit_var'].apply(
|
||||
lambda v: 'rgba(45,198,83,0.7)' if v >= 0 else 'rgba(230,57,70,0.7)'
|
||||
)
|
||||
fig2.add_trace(go.Bar(
|
||||
x=list(range(len(matched))),
|
||||
y=matched['profit_var'],
|
||||
marker_color=colours,
|
||||
name='Profit Variance (B - A)'
|
||||
))
|
||||
fig2.update_layout(
|
||||
height=250,
|
||||
plot_bgcolor='rgba(10,10,15,1)',
|
||||
paper_bgcolor='rgba(10,10,15,1)',
|
||||
font=dict(color='#aaa'),
|
||||
xaxis=dict(gridcolor='rgba(255,255,255,0.04)', title='Trade #'),
|
||||
yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
|
||||
margin=dict(l=60, r=20, t=20, b=40)
|
||||
)
|
||||
st.plotly_chart(fig2, use_container_width=True)
|
||||
|
||||
# ── Matched trade table ────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
st.subheader("Matched Trade Detail")
|
||||
|
||||
def colour_diff(val):
|
||||
try:
|
||||
v = float(str(val).replace('+', ''))
|
||||
if v > 0: return 'color: #2dc653; font-weight: 600'
|
||||
if v < 0: return 'color: #e63946; font-weight: 600'
|
||||
except:
|
||||
pass
|
||||
return 'color: #666'
|
||||
|
||||
def colour_profit_cell(val):
|
||||
try:
|
||||
v = float(str(val).replace(',', ''))
|
||||
if v > 0: return 'background-color: rgba(0,180,0,0.10)'
|
||||
if v < 0: return 'background-color: rgba(180,0,0,0.10)'
|
||||
except:
|
||||
pass
|
||||
return ''
|
||||
|
||||
display = matched[[
|
||||
'A_open_time', 'A_symbol', 'A_type',
|
||||
'A_open_price', 'A_close_price', 'A_profit', 'A_duration',
|
||||
'B_open_time',
|
||||
'B_open_price', 'B_close_price', 'B_profit', 'B_duration',
|
||||
'open_slippage', 'close_slippage', 'profit_var', 'time_diff_min'
|
||||
]].copy()
|
||||
|
||||
display.columns = [
|
||||
'A Open Time', 'Symbol', 'Type',
|
||||
'A Entry', 'A Exit', 'A Profit', 'A Dur(m)',
|
||||
'B Open Time',
|
||||
'B Entry', 'B Exit', 'B Profit', 'B Dur(m)',
|
||||
'Entry Slip', 'Exit Slip', 'Profit Var', 'Time Diff(m)'
|
||||
]
|
||||
|
||||
# Format numeric columns
|
||||
for col in ['A Entry', 'A Exit', 'B Entry', 'B Exit']:
|
||||
if col in display.columns:
|
||||
display[col] = display[col].apply(
|
||||
lambda x: f"{x:.5f}" if pd.notna(x) else '')
|
||||
|
||||
for col in ['A Profit', 'B Profit', 'Profit Var']:
|
||||
display[col] = display[col].apply(
|
||||
lambda x: f"{x:+.2f}" if pd.notna(x) else '')
|
||||
|
||||
for col in ['Entry Slip', 'Exit Slip']:
|
||||
display[col] = display[col].apply(
|
||||
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'
|
||||
)
|
||||
@@ -0,0 +1,215 @@
|
||||
"""
|
||||
view_set_comparator.py
|
||||
======================
|
||||
EA Settings Comparator page — migrated from main dashboard.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
from set_comparator import parse_set_file, build_comparison_df, export_set_file, create_zip
|
||||
|
||||
|
||||
def render():
|
||||
st.markdown("""
|
||||
<style>
|
||||
[data-testid="stDataFrame"] {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
st.title("⚙ EA Settings Comparator")
|
||||
|
||||
# ── Session state init ────────────────────────────────────────────────────
|
||||
if 'ea_files' not in st.session_state: st.session_state['ea_files'] = {}
|
||||
if 'ea_raw' not in st.session_state: st.session_state['ea_raw'] = {}
|
||||
if 'ea_order' not in st.session_state: st.session_state['ea_order'] = {}
|
||||
if 'ea_bytes' not in st.session_state: st.session_state['ea_bytes'] = {}
|
||||
if 'ea_edited' not in st.session_state: st.session_state['ea_edited'] = {}
|
||||
|
||||
# ── Controls ──────────────────────────────────────────────────────────────
|
||||
col1, col2, col3 = st.columns([1, 1, 4])
|
||||
with col1:
|
||||
n_files = st.selectbox("Number of files", list(range(2, 11)), index=0)
|
||||
with col2:
|
||||
st.markdown("<br>", unsafe_allow_html=True)
|
||||
if st.button("🗑 Clear All", type="secondary"):
|
||||
for key in ['ea_files', 'ea_raw', 'ea_order', 'ea_bytes', 'ea_edited']:
|
||||
st.session_state[key] = {}
|
||||
st.rerun()
|
||||
|
||||
# ── File upload slots ─────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
upload_cols = st.columns(min(n_files, 5))
|
||||
for i in range(n_files):
|
||||
with upload_cols[i % 5]:
|
||||
uploaded = st.file_uploader(
|
||||
f"File {i+1}", type=['set'], key=f"ea_upload_{i}"
|
||||
)
|
||||
if uploaded is not None:
|
||||
file_bytes = uploaded.read()
|
||||
fname = uploaded.name
|
||||
params, raw_lines, order = parse_set_file(file_bytes, fname)
|
||||
st.session_state['ea_files'][fname] = params
|
||||
st.session_state['ea_raw'][fname] = raw_lines
|
||||
st.session_state['ea_order'][fname] = order
|
||||
st.session_state['ea_bytes'][fname] = file_bytes
|
||||
if fname not in st.session_state['ea_edited']:
|
||||
st.session_state['ea_edited'][fname] = params.copy()
|
||||
st.success(f"✓ {fname} — {len(params)} params")
|
||||
|
||||
# ── Comparison table ──────────────────────────────────────────────────────
|
||||
files_data = st.session_state['ea_files']
|
||||
|
||||
if len(files_data) >= 2:
|
||||
st.divider()
|
||||
|
||||
filenames = list(files_data.keys())
|
||||
|
||||
col1, col2, col3 = st.columns(3)
|
||||
with col1:
|
||||
source_file = st.selectbox("Source file for comparison", filenames)
|
||||
with col2:
|
||||
pct_threshold = st.slider("Highlight % variation from source", 0, 100, 10)
|
||||
with col3:
|
||||
show_diff_only = st.toggle("Show different rows only", value=False)
|
||||
|
||||
files_list = [
|
||||
(fn, st.session_state['ea_files'][fn],
|
||||
st.session_state['ea_raw'][fn],
|
||||
st.session_state['ea_order'][fn])
|
||||
for fn in filenames
|
||||
]
|
||||
df = build_comparison_df(files_list)
|
||||
|
||||
value_cols = [c for c in df.columns if c != 'Parameter']
|
||||
df['_diff'] = df[value_cols].nunique(axis=1) > 1
|
||||
|
||||
df_display = df[df['_diff']].copy() if show_diff_only else df.copy()
|
||||
df_display = df_display.drop(columns=['_diff'])
|
||||
|
||||
source_vals = files_data.get(source_file, {})
|
||||
|
||||
def style_cells(row):
|
||||
styles = [''] * len(row)
|
||||
param = row['Parameter']
|
||||
src_v = source_vals.get(param, '')
|
||||
for j, col in enumerate(row.index):
|
||||
if col == 'Parameter':
|
||||
continue
|
||||
cell_v = row[col]
|
||||
if col == source_file:
|
||||
styles[j] = 'background-color: rgba(100,100,255,0.15)'
|
||||
continue
|
||||
if cell_v == '' or src_v == '':
|
||||
if cell_v != src_v:
|
||||
styles[j] = 'background-color: rgba(255,180,0,0.2)'
|
||||
continue
|
||||
try:
|
||||
sv = float(src_v)
|
||||
cv = float(cell_v)
|
||||
if sv == 0:
|
||||
if cv != 0:
|
||||
styles[j] = 'background-color: rgba(255,100,100,0.2)'
|
||||
else:
|
||||
pct_diff = abs((cv - sv) / sv) * 100
|
||||
if pct_diff > pct_threshold:
|
||||
styles[j] = 'background-color: rgba(255,100,100,0.2)'
|
||||
elif pct_diff > 0:
|
||||
styles[j] = 'background-color: rgba(255,180,0,0.15)'
|
||||
except:
|
||||
if cell_v != src_v:
|
||||
styles[j] = 'background-color: rgba(255,180,0,0.2)'
|
||||
return styles
|
||||
|
||||
st.markdown(
|
||||
f"**{len(df_display)} parameters** — "
|
||||
f"{int(df[df['_diff']].shape[0])} rows differ across files"
|
||||
)
|
||||
|
||||
styled = df_display.style.apply(style_cells, axis=1)
|
||||
row_height = 35
|
||||
table_h = min(len(df_display) * row_height + 40, 2000)
|
||||
|
||||
col_config = {'Parameter': st.column_config.TextColumn('Parameter', width='medium')}
|
||||
for fn in filenames:
|
||||
col_config[fn] = st.column_config.TextColumn(fn, width='small')
|
||||
|
||||
st.dataframe(
|
||||
styled, width='content', hide_index=True,
|
||||
height=table_h, column_config=col_config
|
||||
)
|
||||
|
||||
# ── Edit & Export ─────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
st.subheader("Edit & Export")
|
||||
|
||||
edit_file = st.selectbox("Select file to edit", filenames, key='ea_edit_sel')
|
||||
|
||||
if edit_file:
|
||||
edited_params = st.session_state['ea_edited'].get(edit_file, {})
|
||||
order = st.session_state['ea_order'].get(edit_file, [])
|
||||
raw_lines = st.session_state['ea_raw'].get(edit_file, {})
|
||||
|
||||
edit_df = pd.DataFrame([
|
||||
{'Parameter': k, 'Value': edited_params.get(k, '')}
|
||||
for k in order
|
||||
])
|
||||
|
||||
edited = st.data_editor(
|
||||
edit_df, width='stretch', hide_index=True, height=400,
|
||||
column_config={
|
||||
'Parameter': st.column_config.TextColumn('Parameter', disabled=True),
|
||||
'Value' : st.column_config.TextColumn('Value'),
|
||||
},
|
||||
key=f"ea_editor_{edit_file}"
|
||||
)
|
||||
|
||||
st.session_state['ea_edited'][edit_file] = dict(
|
||||
zip(edited['Parameter'], edited['Value'].astype(str))
|
||||
)
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
export_bytes = export_set_file(
|
||||
edit_file,
|
||||
st.session_state['ea_edited'][edit_file],
|
||||
raw_lines, order,
|
||||
st.session_state['ea_bytes'][edit_file]
|
||||
)
|
||||
st.download_button(
|
||||
label = f"⬇ Export {edit_file}",
|
||||
data = export_bytes,
|
||||
file_name = edit_file,
|
||||
mime = 'application/octet-stream',
|
||||
key = 'ea_export_single'
|
||||
)
|
||||
|
||||
with col2:
|
||||
all_exports = []
|
||||
for fn in filenames:
|
||||
fb = export_set_file(
|
||||
fn,
|
||||
st.session_state['ea_edited'].get(fn, files_data[fn]),
|
||||
st.session_state['ea_raw'][fn],
|
||||
st.session_state['ea_order'][fn],
|
||||
st.session_state['ea_bytes'][fn]
|
||||
)
|
||||
all_exports.append((fn, fb))
|
||||
zip_bytes = create_zip(all_exports)
|
||||
st.download_button(
|
||||
label = "⬇ Export All as ZIP",
|
||||
data = zip_bytes,
|
||||
file_name = f"ea_settings_{datetime.today().strftime('%Y%m%d')}.zip",
|
||||
mime = 'application/zip',
|
||||
key = 'ea_export_all'
|
||||
)
|
||||
|
||||
elif len(files_data) == 1:
|
||||
st.info("Upload at least 2 files to compare")
|
||||
else:
|
||||
st.info("Upload .set files above to begin comparison")
|
||||
@@ -0,0 +1,37 @@
|
||||
"""
|
||||
pages/settings.py
|
||||
=================
|
||||
Settings page for MT5 Tools dashboard.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
|
||||
|
||||
def render():
|
||||
st.title("⚙️ Settings")
|
||||
|
||||
st.markdown("""
|
||||
<div class="info-card">
|
||||
MT5 Tools — settings and information.
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
st.subheader("About")
|
||||
st.markdown("""
|
||||
**MT5 Tools** is a standalone trade analysis and comparison dashboard.
|
||||
|
||||
**Supported file formats:**
|
||||
- MT5 Account History HTML export (`.htm` / `.html`)
|
||||
- MT5 Strategy Tester Backtest Report (`.htm` / `.html`)
|
||||
- Quant Analyzer CSV export (`listOfTrades_*.csv`)
|
||||
|
||||
**Pages:**
|
||||
- **Trade Analysis** — statistics, equity curves, day/hour breakdown for a single report
|
||||
- **Trade Compare** — match and compare two reports to measure slippage and variance
|
||||
""")
|
||||
|
||||
st.subheader("Requirements")
|
||||
st.code("pip install streamlit streamlit-option-menu pandas plotly", language="bash")
|
||||
|
||||
st.subheader("Launch")
|
||||
st.code("streamlit run app.py", language="bash")
|
||||
@@ -0,0 +1,333 @@
|
||||
"""
|
||||
pages/trade_analysis.py
|
||||
=======================
|
||||
MT5 Trade Analysis page — migrated from main dashboard.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import plotly.graph_objects as go
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
from mt5_parser import detect_and_parse, calc_stats
|
||||
|
||||
|
||||
def render():
|
||||
st.title("📊 Trade Analysis")
|
||||
|
||||
# ── Session state ─────────────────────────────────────────────────────────
|
||||
if 'ta_df' not in st.session_state:
|
||||
st.session_state['ta_df'] = None
|
||||
st.session_state['ta_format'] = None
|
||||
|
||||
# ── File upload ───────────────────────────────────────────────────────────
|
||||
col1, col2 = st.columns([4, 1])
|
||||
with col1:
|
||||
uploaded = st.file_uploader(
|
||||
"Upload MT5 Report (HTM/HTML) or Quant Analyzer CSV",
|
||||
type=['html', 'htm', 'csv'],
|
||||
key='ta_upload'
|
||||
)
|
||||
with col2:
|
||||
st.markdown("<br>", unsafe_allow_html=True)
|
||||
if st.button("🗑 Clear", key='ta_clear'):
|
||||
st.session_state['ta_df'] = None
|
||||
st.session_state['ta_format'] = None
|
||||
st.rerun()
|
||||
|
||||
if uploaded:
|
||||
df, fmt = detect_and_parse(uploaded.read(), uploaded.name)
|
||||
if df is not None:
|
||||
st.session_state['ta_df'] = df
|
||||
st.session_state['ta_format'] = fmt
|
||||
st.success(f"✓ Loaded {len(df)} trades — {fmt}")
|
||||
else:
|
||||
st.error("Could not parse report — check file format")
|
||||
|
||||
df_all = st.session_state['ta_df']
|
||||
fmt = st.session_state['ta_format']
|
||||
|
||||
if df_all is None or len(df_all) == 0:
|
||||
st.markdown("""
|
||||
<div class="info-card">
|
||||
Upload an MT5 account history report (.htm/.html), MT5 backtest report,
|
||||
or a Quant Analyzer CSV export to begin analysis.
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
return
|
||||
|
||||
if fmt:
|
||||
st.caption(f"Format detected: **{fmt}** · {len(df_all)} total trades")
|
||||
|
||||
# ── Filters ───────────────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
fc1, fc2, fc3, fc4 = st.columns(4)
|
||||
|
||||
with fc1:
|
||||
date_min = df_all['open_time'].min().date()
|
||||
date_max = df_all['open_time'].max().date()
|
||||
date_from = st.date_input("From", value=date_min, min_value=date_min,
|
||||
max_value=date_max, key='ta_from')
|
||||
date_to = st.date_input("To", value=date_max, min_value=date_min,
|
||||
max_value=date_max, key='ta_to')
|
||||
|
||||
with fc2:
|
||||
symbols = sorted(df_all['symbol'].dropna().unique().tolist())
|
||||
sel_symbol = st.multiselect("Symbol", symbols, key='ta_sym')
|
||||
|
||||
with fc3:
|
||||
strategies = sorted(df_all['strategy'].dropna().unique().tolist())
|
||||
sel_strategy = st.multiselect("Strategy / EA", strategies, key='ta_strat')
|
||||
|
||||
with fc4:
|
||||
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
|
||||
sel_days = st.multiselect("Day of week", days, key='ta_days')
|
||||
sel_type = st.multiselect("Type", ['buy', 'sell'], key='ta_type')
|
||||
|
||||
# Apply filters
|
||||
df = df_all.copy()
|
||||
df = df[(df['open_time'].dt.date >= date_from) &
|
||||
(df['open_time'].dt.date <= date_to)]
|
||||
if sel_symbol:
|
||||
df = df[df['symbol'].isin(sel_symbol)]
|
||||
if sel_strategy:
|
||||
df = df[df['strategy'].isin(sel_strategy)]
|
||||
if sel_days:
|
||||
df = df[df['day_of_week'].isin(sel_days)]
|
||||
if sel_type:
|
||||
df = df[df['type'].isin(sel_type)]
|
||||
|
||||
st.caption(f"Showing **{len(df)}** trades after filters")
|
||||
|
||||
# ── Analysis mode ─────────────────────────────────────────────────────────
|
||||
mode = st.radio(
|
||||
"Analysis mode",
|
||||
["Overall", "By Strategy", "By Symbol", "By Day of Week"],
|
||||
horizontal=True, key='ta_mode'
|
||||
)
|
||||
st.divider()
|
||||
|
||||
# ── Helpers ───────────────────────────────────────────────────────────────
|
||||
def render_stats(stats, label=""):
|
||||
if label:
|
||||
st.markdown(f"**{label}**")
|
||||
|
||||
c1, c2, c3, c4, c5 = st.columns(5)
|
||||
c1.metric("Net Profit", f"${stats['net_profit']:,.2f}")
|
||||
c2.metric("Win Rate", f"{stats['win_rate']}%")
|
||||
c3.metric("Profit Factor", f"{stats['profit_factor']}")
|
||||
c4.metric("R:R Ratio", f"{stats['rr_ratio']}")
|
||||
c5.metric("Expectancy", f"${stats['expectancy']:,.2f}")
|
||||
|
||||
c1, c2, c3, c4, c5 = st.columns(5)
|
||||
c1.metric("Total Trades", stats['total_trades'])
|
||||
c2.metric("Avg Win", f"${stats['avg_win']:,.2f}")
|
||||
c3.metric("Avg Loss", f"${stats['avg_loss']:,.2f}")
|
||||
c4.metric("Max DD", f"${stats['max_drawdown']:,.2f}")
|
||||
c5.metric("Best Trade", f"${stats['best_trade']:,.2f}")
|
||||
|
||||
c1, c2, c3, c4, c5 = st.columns(5)
|
||||
c1.metric("Max Consec Wins", stats['max_consec_wins'])
|
||||
c2.metric("Max Consec Losses", stats['max_consec_losses'])
|
||||
c3.metric("Avg Win Dur", f"{stats['avg_win_duration']}m")
|
||||
c4.metric("Avg Loss Dur", f"{stats['avg_loss_duration']}m")
|
||||
c5.metric("Worst Trade", f"${stats['worst_trade']:,.2f}")
|
||||
|
||||
c1, c2, c3, c4 = st.columns(4)
|
||||
c1.metric("Long Trades", stats['long_trades'])
|
||||
c2.metric("Long Win Rate", f"{stats['long_win_rate']}%")
|
||||
c3.metric("Short Trades", stats['short_trades'])
|
||||
c4.metric("Short Win Rate",f"{stats['short_win_rate']}%")
|
||||
|
||||
def render_equity_curve(df_plot, label="Equity Curve"):
|
||||
df_s = df_plot.sort_values('close_time').copy()
|
||||
df_s['cumulative'] = df_s['net_profit'].cumsum()
|
||||
fig = go.Figure()
|
||||
fig.add_trace(go.Scatter(
|
||||
x=df_s['close_time'], y=df_s['cumulative'],
|
||||
mode='lines',
|
||||
line=dict(color='#7c6af7', width=2),
|
||||
fill='tozeroy',
|
||||
fillcolor='rgba(124,106,247,0.08)',
|
||||
name='Equity'
|
||||
))
|
||||
fig.update_layout(
|
||||
title=label, height=300,
|
||||
plot_bgcolor='rgba(10,10,15,1)',
|
||||
paper_bgcolor='rgba(10,10,15,1)',
|
||||
font=dict(color='#aaa', family='JetBrains Mono'),
|
||||
xaxis=dict(gridcolor='rgba(255,255,255,0.04)'),
|
||||
yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
|
||||
margin=dict(l=60, r=20, t=40, b=40)
|
||||
)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
def render_dow_chart(df_plot):
|
||||
dow_order = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
|
||||
dow = df_plot.groupby('day_of_week').agg(
|
||||
trades = ('net_profit', 'count'),
|
||||
net_profit = ('net_profit', 'sum'),
|
||||
win_rate = ('win', lambda x: round(x.mean()*100, 1))
|
||||
).reindex([d for d in dow_order if d in df_plot['day_of_week'].unique()])
|
||||
|
||||
wins_dow = df_plot[df_plot['win']].groupby('day_of_week')['net_profit'].sum().reindex(dow.index, fill_value=0)
|
||||
losses_dow = df_plot[~df_plot['win']].groupby('day_of_week')['net_profit'].sum().reindex(dow.index, fill_value=0)
|
||||
|
||||
fig = go.Figure()
|
||||
fig.add_trace(go.Bar(x=dow.index, y=wins_dow, name='Profit', marker_color='rgba(45,198,83,0.8)'))
|
||||
fig.add_trace(go.Bar(x=dow.index, y=losses_dow, name='Loss', marker_color='rgba(230,57,70,0.8)'))
|
||||
fig.update_layout(
|
||||
title='P&L by Day of Week', height=280, barmode='relative',
|
||||
plot_bgcolor='rgba(10,10,15,1)', paper_bgcolor='rgba(10,10,15,1)',
|
||||
font=dict(color='#aaa'), margin=dict(l=60, r=20, t=40, b=40),
|
||||
xaxis=dict(gridcolor='rgba(255,255,255,0.04)'),
|
||||
yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
|
||||
legend=dict(bgcolor='rgba(0,0,0,0.3)')
|
||||
)
|
||||
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(10,10,15,1)', paper_bgcolor='rgba(10,10,15,1)',
|
||||
font=dict(color='#aaa'), margin=dict(l=60, r=20, t=40, b=40),
|
||||
xaxis=dict(gridcolor='rgba(255,255,255,0.04)', title='Hour (UTC)'),
|
||||
yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
|
||||
legend=dict(bgcolor='rgba(0,0,0,0.3)')
|
||||
)
|
||||
st.plotly_chart(fig, use_container_width=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)
|
||||
render_stats(stats, "Overall Statistics")
|
||||
render_equity_curve(df)
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
render_dow_chart(df)
|
||||
with col2:
|
||||
render_hour_chart(df)
|
||||
|
||||
elif mode == "By Strategy":
|
||||
strats = sorted(df['strategy'].dropna().unique().tolist())
|
||||
if not strats:
|
||||
st.info("No strategies found")
|
||||
else:
|
||||
st.subheader("Strategy Comparison")
|
||||
rows = []
|
||||
for s in strats:
|
||||
sdf = df[df['strategy'] == s]
|
||||
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'
|
||||
)
|
||||
@@ -0,0 +1,422 @@
|
||||
"""
|
||||
pages/trade_compare.py
|
||||
======================
|
||||
Side-by-side comparison of two trade history files.
|
||||
Matches trades by symbol + type + open time within a tolerance window.
|
||||
Highlights slippage, profit variance, and timing differences.
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
from mt5_parser import detect_and_parse, calc_stats
|
||||
|
||||
|
||||
# ── Match trades ──────────────────────────────────────────────────────────────
|
||||
|
||||
def match_trades(df_a, df_b, tolerance_hours):
|
||||
"""
|
||||
Match trades between two DataFrames.
|
||||
Match criteria: same symbol_base + same type + open_time within tolerance.
|
||||
Returns DataFrame of matched pairs with diff columns.
|
||||
"""
|
||||
tol = pd.Timedelta(hours=tolerance_hours)
|
||||
matched = []
|
||||
used_b = set()
|
||||
|
||||
for i, a in df_a.iterrows():
|
||||
best_match = None
|
||||
best_delta = tol + pd.Timedelta(seconds=1)
|
||||
|
||||
for j, b in df_b.iterrows():
|
||||
if j in used_b:
|
||||
continue
|
||||
if a['symbol_base'] != b['symbol_base']:
|
||||
continue
|
||||
if a['type'] != b['type']:
|
||||
continue
|
||||
delta = abs(a['open_time'] - b['open_time'])
|
||||
if delta <= tol and delta < best_delta:
|
||||
best_delta = delta
|
||||
best_match = (j, b)
|
||||
|
||||
if best_match:
|
||||
j, b = best_match
|
||||
used_b.add(j)
|
||||
|
||||
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
|
||||
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
|
||||
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
|
||||
time_diff = round((b['open_time'] - a['open_time']).total_seconds() / 60, 1)
|
||||
dur_diff = round(float(b.get('duration_min', 0) or 0) - float(a.get('duration_min', 0) or 0), 1)
|
||||
|
||||
matched.append({
|
||||
# File A
|
||||
'A_open_time' : a['open_time'],
|
||||
'A_close_time' : a['close_time'],
|
||||
'A_symbol' : a['symbol'],
|
||||
'A_type' : a['type'],
|
||||
'A_volume' : a.get('volume'),
|
||||
'A_open_price' : a.get('open_price'),
|
||||
'A_close_price': a.get('close_price'),
|
||||
'A_profit' : a.get('net_profit'),
|
||||
'A_duration' : a.get('duration_min'),
|
||||
# File B
|
||||
'B_open_time' : b['open_time'],
|
||||
'B_close_time' : b['close_time'],
|
||||
'B_symbol' : b['symbol'],
|
||||
'B_type' : b['type'],
|
||||
'B_volume' : b.get('volume'),
|
||||
'B_open_price' : b.get('open_price'),
|
||||
'B_close_price': b.get('close_price'),
|
||||
'B_profit' : b.get('net_profit'),
|
||||
'B_duration' : b.get('duration_min'),
|
||||
# Differences
|
||||
'open_slippage' : open_slip,
|
||||
'close_slippage': close_slip,
|
||||
'profit_var' : profit_var,
|
||||
'time_diff_min' : time_diff,
|
||||
'duration_diff' : dur_diff,
|
||||
})
|
||||
|
||||
return pd.DataFrame(matched)
|
||||
|
||||
|
||||
# ── Render ────────────────────────────────────────────────────────────────────
|
||||
|
||||
def render():
|
||||
st.title("🔄 Trade Compare")
|
||||
|
||||
st.markdown("""
|
||||
<div class="info-card">
|
||||
Compare two trade history files — backtest vs real account, or any two exports.
|
||||
Trades are matched by symbol, direction, and open time within a configurable
|
||||
tolerance window to account for gaps, slippage, and market open variations.
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# ── Session state ─────────────────────────────────────────────────────────
|
||||
for k in ['tc_df_a', 'tc_df_b', 'tc_fmt_a', 'tc_fmt_b']:
|
||||
if k not in st.session_state:
|
||||
st.session_state[k] = None
|
||||
|
||||
# ── File upload ───────────────────────────────────────────────────────────
|
||||
st.subheader("Load Files")
|
||||
col_a, col_b = st.columns(2)
|
||||
|
||||
with col_a:
|
||||
st.markdown("**File A** — Reference (e.g. Backtest)")
|
||||
up_a = st.file_uploader("Upload File A", type=['html','htm','csv'], key='tc_up_a')
|
||||
if up_a:
|
||||
df_a, fmt_a = detect_and_parse(up_a.read(), up_a.name)
|
||||
if df_a is not None:
|
||||
st.session_state['tc_df_a'] = df_a
|
||||
st.session_state['tc_fmt_a'] = fmt_a
|
||||
st.success(f"✓ {len(df_a)} trades — {fmt_a}")
|
||||
else:
|
||||
st.error("Could not parse File A")
|
||||
if st.session_state['tc_df_a'] is not None:
|
||||
st.caption(f"Loaded: **{st.session_state['tc_fmt_a']}** · {len(st.session_state['tc_df_a'])} trades")
|
||||
|
||||
with col_b:
|
||||
st.markdown("**File B** — Comparison (e.g. Real Account)")
|
||||
up_b = st.file_uploader("Upload File B", type=['html','htm','csv'], key='tc_up_b')
|
||||
if up_b:
|
||||
df_b, fmt_b = detect_and_parse(up_b.read(), up_b.name)
|
||||
if df_b is not None:
|
||||
st.session_state['tc_df_b'] = df_b
|
||||
st.session_state['tc_fmt_b'] = fmt_b
|
||||
st.success(f"✓ {len(df_b)} trades — {fmt_b}")
|
||||
else:
|
||||
st.error("Could not parse File B")
|
||||
if st.session_state['tc_df_b'] is not None:
|
||||
st.caption(f"Loaded: **{st.session_state['tc_fmt_b']}** · {len(st.session_state['tc_df_b'])} trades")
|
||||
|
||||
df_a = st.session_state['tc_df_a']
|
||||
df_b = st.session_state['tc_df_b']
|
||||
|
||||
if df_a is None or df_b is None:
|
||||
return
|
||||
|
||||
# ── Filters ───────────────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
st.subheader("Filters")
|
||||
|
||||
fa1, fa2, fa3 = st.columns(3)
|
||||
fb1, fb2, fb3 = st.columns(3)
|
||||
|
||||
with fa1:
|
||||
st.markdown("**File A filters**")
|
||||
with fb1:
|
||||
st.markdown("**File B filters**")
|
||||
|
||||
col1, col2, col3, col4, col5, col6 = st.columns(6)
|
||||
|
||||
with col1:
|
||||
a_date_min = df_a['open_time'].min().date()
|
||||
a_date_max = df_a['open_time'].max().date()
|
||||
a_from = st.date_input("A — From", value=a_date_min, min_value=a_date_min,
|
||||
max_value=a_date_max, key='tc_a_from')
|
||||
a_to = st.date_input("A — To", value=a_date_max, min_value=a_date_min,
|
||||
max_value=a_date_max, key='tc_a_to')
|
||||
|
||||
with col2:
|
||||
a_syms = sorted(df_a['symbol'].dropna().unique().tolist())
|
||||
a_sel_sym = st.multiselect("A — Symbol", a_syms, key='tc_a_sym')
|
||||
|
||||
with col3:
|
||||
a_strats = sorted(df_a['strategy'].dropna().unique().tolist())
|
||||
a_sel_strat = st.multiselect("A — Strategy", a_strats, key='tc_a_strat')
|
||||
a_sel_type = st.multiselect("A — Type", ['buy', 'sell'], key='tc_a_type')
|
||||
|
||||
with col4:
|
||||
b_date_min = df_b['open_time'].min().date()
|
||||
b_date_max = df_b['open_time'].max().date()
|
||||
b_from = st.date_input("B — From", value=b_date_min, min_value=b_date_min,
|
||||
max_value=b_date_max, key='tc_b_from')
|
||||
b_to = st.date_input("B — To", value=b_date_max, min_value=b_date_min,
|
||||
max_value=b_date_max, key='tc_b_to')
|
||||
|
||||
with col5:
|
||||
b_syms = sorted(df_b['symbol'].dropna().unique().tolist())
|
||||
b_sel_sym = st.multiselect("B — Symbol", b_syms, key='tc_b_sym')
|
||||
|
||||
with col6:
|
||||
b_strats = sorted(df_b['strategy'].dropna().unique().tolist())
|
||||
b_sel_strat = st.multiselect("B — Strategy", b_strats, key='tc_b_strat')
|
||||
b_sel_type = st.multiselect("B — Type", ['buy', 'sell'], key='tc_b_type')
|
||||
|
||||
# ── Matching tolerance ────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
col_tol, col_run = st.columns([3, 1])
|
||||
with col_tol:
|
||||
tolerance = st.slider(
|
||||
"Match tolerance (hours) — max time difference between A and B open times",
|
||||
min_value=1, max_value=24, value=4, step=1,
|
||||
help="Trades within this window are considered the same setup. "
|
||||
"Increase for daily charts, decrease for intraday."
|
||||
)
|
||||
with col_run:
|
||||
st.markdown("<br>", unsafe_allow_html=True)
|
||||
run = st.button("🔍 Match Trades", type="primary", use_container_width=True)
|
||||
|
||||
if not run and 'tc_matched' not in st.session_state:
|
||||
return
|
||||
|
||||
# Apply filters
|
||||
fa = df_a.copy()
|
||||
fa = fa[(fa['open_time'].dt.date >= a_from) & (fa['open_time'].dt.date <= a_to)]
|
||||
if a_sel_sym: fa = fa[fa['symbol'].isin(a_sel_sym)]
|
||||
if a_sel_strat: fa = fa[fa['strategy'].isin(a_sel_strat)]
|
||||
if a_sel_type: fa = fa[fa['type'].isin(a_sel_type)]
|
||||
|
||||
fb = df_b.copy()
|
||||
fb = fb[(fb['open_time'].dt.date >= b_from) & (fb['open_time'].dt.date <= b_to)]
|
||||
if b_sel_sym: fb = fb[fb['symbol'].isin(b_sel_sym)]
|
||||
if b_sel_strat: fb = fb[fb['strategy'].isin(b_sel_strat)]
|
||||
if b_sel_type: fb = fb[fb['type'].isin(b_sel_type)]
|
||||
|
||||
if run:
|
||||
with st.spinner("Matching trades..."):
|
||||
matched = match_trades(fa, fb, tolerance)
|
||||
st.session_state['tc_matched'] = matched
|
||||
st.session_state['tc_fa_len'] = len(fa)
|
||||
st.session_state['tc_fb_len'] = len(fb)
|
||||
|
||||
matched = st.session_state.get('tc_matched', pd.DataFrame())
|
||||
fa_len = st.session_state.get('tc_fa_len', len(fa))
|
||||
fb_len = st.session_state.get('tc_fb_len', len(fb))
|
||||
|
||||
if matched is None or len(matched) == 0:
|
||||
st.warning("No matching trades found — try increasing the tolerance window or adjusting filters.")
|
||||
return
|
||||
|
||||
# ── Summary stats ─────────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
st.subheader("Match Summary")
|
||||
|
||||
m1, m2, m3, m4, m5 = st.columns(5)
|
||||
m1.metric("File A Trades", fa_len)
|
||||
m2.metric("File B Trades", fb_len)
|
||||
m3.metric("Matched Pairs", len(matched))
|
||||
m4.metric("Unmatched A", fa_len - len(matched))
|
||||
m5.metric("Unmatched B", fb_len - len(matched))
|
||||
|
||||
st.divider()
|
||||
|
||||
# ── Aggregate comparison ───────────────────────────────────────────────────
|
||||
st.subheader("Aggregate Comparison")
|
||||
|
||||
ac1, ac2 = st.columns(2)
|
||||
|
||||
with ac1:
|
||||
st.markdown("**File A (Reference)**")
|
||||
a_net = matched['A_profit'].sum()
|
||||
a_wr = (matched['A_profit'] > 0).mean() * 100
|
||||
a_avg = matched['A_profit'].mean()
|
||||
a_dur = matched['A_duration'].mean() if 'A_duration' in matched else None
|
||||
st.metric("Net Profit", f"${a_net:,.2f}")
|
||||
st.metric("Win Rate", f"{a_wr:.1f}%")
|
||||
st.metric("Avg Profit", f"${a_avg:,.2f}")
|
||||
if a_dur:
|
||||
st.metric("Avg Duration", f"{a_dur:.0f}m")
|
||||
|
||||
with ac2:
|
||||
st.markdown("**File B (Comparison)**")
|
||||
b_net = matched['B_profit'].sum()
|
||||
b_wr = (matched['B_profit'] > 0).mean() * 100
|
||||
b_avg = matched['B_profit'].mean()
|
||||
b_dur = matched['B_duration'].mean() if 'B_duration' in matched else None
|
||||
delta_net = b_net - a_net
|
||||
st.metric("Net Profit", f"${b_net:,.2f}",
|
||||
delta=f"{delta_net:+.2f}", delta_color="normal")
|
||||
st.metric("Win Rate", f"{b_wr:.1f}%",
|
||||
delta=f"{b_wr - a_wr:+.1f}%", delta_color="normal")
|
||||
st.metric("Avg Profit", f"${b_avg:,.2f}",
|
||||
delta=f"{b_avg - a_avg:+.2f}", delta_color="normal")
|
||||
if b_dur and a_dur:
|
||||
st.metric("Avg Duration", f"{b_dur:.0f}m",
|
||||
delta=f"{b_dur - a_dur:+.0f}m", delta_color="off")
|
||||
|
||||
# ── Slippage summary ───────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
st.subheader("Slippage & Variance Summary")
|
||||
|
||||
sc1, sc2, sc3, sc4 = st.columns(4)
|
||||
avg_open_slip = matched['open_slippage'].mean()
|
||||
avg_close_slip = matched['close_slip'].mean() if 'close_slip' in matched else matched['close_slippage'].mean()
|
||||
avg_profit_var = matched['profit_var'].mean()
|
||||
avg_time_diff = matched['time_diff_min'].mean()
|
||||
|
||||
sc1.metric("Avg Entry Slippage", f"{avg_open_slip:+.5f}" if pd.notna(avg_open_slip) else "N/A",
|
||||
help="B open price minus A open price. Positive = B filled higher.")
|
||||
sc2.metric("Avg Exit Slippage", f"{avg_close_slip:+.5f}" if pd.notna(avg_close_slip) else "N/A",
|
||||
help="B close price minus A close price.")
|
||||
sc3.metric("Avg Profit Variance", f"${avg_profit_var:+.2f}" if pd.notna(avg_profit_var) else "N/A",
|
||||
help="B net profit minus A net profit per trade.")
|
||||
sc4.metric("Avg Time Difference", f"{avg_time_diff:+.0f}m" if pd.notna(avg_time_diff) else "N/A",
|
||||
help="B open time minus A open time in minutes.")
|
||||
|
||||
# ── Equity curve overlay ───────────────────────────────────────────────────
|
||||
st.divider()
|
||||
st.subheader("Equity Curve Overlay")
|
||||
|
||||
m_sorted = matched.sort_values('A_open_time')
|
||||
fig = go.Figure()
|
||||
fig.add_trace(go.Scatter(
|
||||
x=m_sorted['A_open_time'],
|
||||
y=m_sorted['A_profit'].cumsum(),
|
||||
mode='lines', name='File A',
|
||||
line=dict(color='#7c6af7', width=2),
|
||||
fill='tozeroy', fillcolor='rgba(124,106,247,0.05)'
|
||||
))
|
||||
fig.add_trace(go.Scatter(
|
||||
x=m_sorted['B_open_time'],
|
||||
y=m_sorted['B_profit'].cumsum(),
|
||||
mode='lines', name='File B',
|
||||
line=dict(color='#2dc653', width=2),
|
||||
fill='tozeroy', fillcolor='rgba(45,198,83,0.05)'
|
||||
))
|
||||
fig.update_layout(
|
||||
height=320,
|
||||
plot_bgcolor='rgba(10,10,15,1)',
|
||||
paper_bgcolor='rgba(10,10,15,1)',
|
||||
font=dict(color='#aaa', family='JetBrains Mono'),
|
||||
xaxis=dict(gridcolor='rgba(255,255,255,0.04)'),
|
||||
yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
|
||||
legend=dict(bgcolor='rgba(0,0,0,0.3)'),
|
||||
margin=dict(l=60, r=20, t=20, b=40)
|
||||
)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
# ── Profit variance scatter ────────────────────────────────────────────────
|
||||
st.subheader("Profit Variance per Trade")
|
||||
fig2 = go.Figure()
|
||||
colours = matched['profit_var'].apply(
|
||||
lambda v: 'rgba(45,198,83,0.7)' if v >= 0 else 'rgba(230,57,70,0.7)'
|
||||
)
|
||||
fig2.add_trace(go.Bar(
|
||||
x=list(range(len(matched))),
|
||||
y=matched['profit_var'],
|
||||
marker_color=colours,
|
||||
name='Profit Variance (B - A)'
|
||||
))
|
||||
fig2.update_layout(
|
||||
height=250,
|
||||
plot_bgcolor='rgba(10,10,15,1)',
|
||||
paper_bgcolor='rgba(10,10,15,1)',
|
||||
font=dict(color='#aaa'),
|
||||
xaxis=dict(gridcolor='rgba(255,255,255,0.04)', title='Trade #'),
|
||||
yaxis=dict(gridcolor='rgba(255,255,255,0.04)', tickprefix='$'),
|
||||
margin=dict(l=60, r=20, t=20, b=40)
|
||||
)
|
||||
st.plotly_chart(fig2, use_container_width=True)
|
||||
|
||||
# ── Matched trade table ────────────────────────────────────────────────────
|
||||
st.divider()
|
||||
st.subheader("Matched Trade Detail")
|
||||
|
||||
def colour_diff(val):
|
||||
try:
|
||||
v = float(str(val).replace('+', ''))
|
||||
if v > 0: return 'color: #2dc653; font-weight: 600'
|
||||
if v < 0: return 'color: #e63946; font-weight: 600'
|
||||
except:
|
||||
pass
|
||||
return 'color: #666'
|
||||
|
||||
def colour_profit_cell(val):
|
||||
try:
|
||||
v = float(str(val).replace(',', ''))
|
||||
if v > 0: return 'background-color: rgba(0,180,0,0.10)'
|
||||
if v < 0: return 'background-color: rgba(180,0,0,0.10)'
|
||||
except:
|
||||
pass
|
||||
return ''
|
||||
|
||||
display = matched[[
|
||||
'A_open_time', 'A_symbol', 'A_type',
|
||||
'A_open_price', 'A_close_price', 'A_profit', 'A_duration',
|
||||
'B_open_time',
|
||||
'B_open_price', 'B_close_price', 'B_profit', 'B_duration',
|
||||
'open_slippage', 'close_slippage', 'profit_var', 'time_diff_min'
|
||||
]].copy()
|
||||
|
||||
display.columns = [
|
||||
'A Open Time', 'Symbol', 'Type',
|
||||
'A Entry', 'A Exit', 'A Profit', 'A Dur(m)',
|
||||
'B Open Time',
|
||||
'B Entry', 'B Exit', 'B Profit', 'B Dur(m)',
|
||||
'Entry Slip', 'Exit Slip', 'Profit Var', 'Time Diff(m)'
|
||||
]
|
||||
|
||||
# Format numeric columns
|
||||
for col in ['A Entry', 'A Exit', 'B Entry', 'B Exit']:
|
||||
if col in display.columns:
|
||||
display[col] = display[col].apply(
|
||||
lambda x: f"{x:.5f}" if pd.notna(x) else '')
|
||||
|
||||
for col in ['A Profit', 'B Profit', 'Profit Var']:
|
||||
display[col] = display[col].apply(
|
||||
lambda x: f"{x:+.2f}" if pd.notna(x) else '')
|
||||
|
||||
for col in ['Entry Slip', 'Exit Slip']:
|
||||
display[col] = display[col].apply(
|
||||
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'
|
||||
)
|
||||
Reference in New Issue
Block a user