feat: Live MT5 EAs page + parser improvements
Live MT5 EAs page (view_ftp_tracker.py): - FTP-based multi-account tracker replacing view_mt5_tracker.py - Account configuration with add/remove, FTP folder validation, Demo/Personal/Prop types - Prop account fields: profit target %, max loss %, daily loss % with EA hard stop banner - Account summary cards: recovery factor, loss streak, stagnation days, today P&L, prop progress bars - Calendar view: month/week/year, Mon-Fri only, weekly total column, $ or % toggle - Open positions table parsed from MT5 HTML Open Positions section - Symbol correlation heatmap (collapsible) - Trade analysis section with full stats, equity/drawdown/daily P&L, DOW/hour, monthly table - Analysis modes: Overall, By Account, By Symbol, By Algo, By Day of Week - Dynamic combined balance field auto-updates from selected accounts - Auto-refresh polling with session state timestamp guard - Report date extracted from MT5 HTML header (Date: field) - Drawdown $ / % toggle with spline smoothing mt5_parser.py: - calc_stats(df, deposit=0.0) — deposit-aware balance drawdown matching MT5 Balance Drawdown Maximal - max_drawdown_pct and peak_equity added to calc_stats return - peak_at_dd uses .loc[] fix (IndexError on filtered DataFrames) - parse_open_positions() — parses Open Positions section from MT5 account HTML - extract_strategy() — filters sl/tp/so close-reason comments, maps nan to Manual ftp_sync_cli.py: - New CLI tool for FTP connection testing and cache population MT5Tools_FTP_Setup_Guide.docx: - FileZilla Server setup, MT5 publisher config, CLI verification, troubleshooting
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+130
-21
@@ -42,7 +42,36 @@ def _to_dt(s, fmt='%Y.%m.%d %H:%M:%S'):
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return pd.to_datetime(s, format=fmt, errors='coerce')
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def _enrich(df):
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def _strategy_from_filename(filename):
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"""
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Derive a clean strategy name from a filename.
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Strips leading date prefix (DD_MM_YYYY_ or YYYY_MM_DD_) and file extension.
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E.g. '22_03_2026GoldPhantomModerate.csv' -> 'GoldPhantomModerate'
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'GoldPhantom_XAUUSD_Daily_OHLC_A.htm' -> 'GoldPhantom'
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"""
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import os
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stem = os.path.splitext(os.path.basename(filename))[0]
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# Strip leading date prefix like 22_03_2026 or 2026_03_22 (with optional separator)
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stem = re.sub(r'^\d{2}_\d{2}_\d{4}', '', stem)
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stem = re.sub(r'^\d{4}_\d{2}_\d{2}', '', stem)
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# Strip leading underscores/hyphens left after date removal
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stem = stem.lstrip('_-')
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# If underscore-delimited, take only parts that look like a name (not symbol/period/model)
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parts = stem.split('_')
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clean = []
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for p in parts:
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# Stop at parts that look like: instrument suffix (.a), timeframe (H1/M15/Daily),
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# model label (OHLC/EVERYTICK), or single uppercase letter (instance A/B/C)
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if re.match(r'^(H\d+|M\d+|Daily|Weekly|Monthly|OHLC|EVERYTICK|CTRLPTS|[A-Z])$', p):
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break
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if re.match(r'^[A-Z]{3,8}(\.a)?$', p):
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break
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clean.append(p)
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result = '_'.join(clean) if clean else stem
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return result if result else stem
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def _enrich(df, fallback_strategy=None):
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"""Add derived columns common to all formats."""
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df['open_time'] = pd.to_datetime(df['open_time'], errors='coerce')
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df['close_time'] = pd.to_datetime(df['close_time'], errors='coerce')
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@@ -70,6 +99,10 @@ def _enrich(df):
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if 'comment' not in df.columns:
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df['comment'] = ''
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df['strategy'] = df['comment'].apply(extract_strategy)
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# If every trade resolved to 'Manual' and a fallback name was supplied
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# (e.g. derived from the filename), use it instead.
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if fallback_strategy and (df['strategy'] == 'Manual').all():
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df['strategy'] = fallback_strategy
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# Normalise symbol — strip .a suffix for display matching
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df['symbol_base'] = df['symbol'].str.replace(r'\.[a-z]+$', '', regex=True).str.upper()
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return df
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@@ -77,7 +110,7 @@ def _enrich(df):
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# ── Format 1: Real Account HTM ────────────────────────────────────────────────
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def parse_mt5_report(file_bytes):
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def parse_mt5_report(file_bytes, fallback_strategy=None):
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"""Parse MT5 real account HTML trade history report."""
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text = _decode(file_bytes)
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rows = re.findall(r'<tr[^>]*>(.*?)</tr>', text, re.DOTALL)
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@@ -120,12 +153,12 @@ def parse_mt5_report(file_bytes):
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df = pd.DataFrame(trades)
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df['source'] = 'real'
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return _enrich(df)
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return _enrich(df, fallback_strategy=fallback_strategy)
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# ── Format 2: Backtest HTM ────────────────────────────────────────────────────
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def parse_backtest_report(file_bytes):
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def parse_backtest_report(file_bytes, fallback_strategy=None):
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"""
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Parse MT5 Strategy Tester HTML report.
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Pairs in/out deals into complete trades.
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@@ -203,7 +236,7 @@ def parse_backtest_report(file_bytes):
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'commission' : _to_float(entry.get('commission', 0)),
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'swap' : _to_float(deal.get('swap', 0)),
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'profit' : _to_float(deal.get('profit', 0)),
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'comment' : deal.get('comment', ''),
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'comment' : entry.get('comment', '') or deal.get('comment', ''),
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'position' : entry.get('deal', ''),
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})
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@@ -212,12 +245,12 @@ def parse_backtest_report(file_bytes):
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df = pd.DataFrame(trades)
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df['source'] = 'backtest'
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return _enrich(df)
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return _enrich(df, fallback_strategy=fallback_strategy)
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# ── Format 3: Quant Analyzer CSV ─────────────────────────────────────────────
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def parse_quant_csv(file_bytes):
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def parse_quant_csv(file_bytes, fallback_strategy=None):
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"""Parse Quant Analyzer listOfTrades CSV export."""
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try:
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text = file_bytes.decode('utf-8-sig')
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@@ -286,7 +319,71 @@ def parse_quant_csv(file_bytes):
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if extra in df_raw.columns:
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df[extra] = pd.to_numeric(df_raw[extra], errors='coerce')
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return _enrich(df)
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return _enrich(df, fallback_strategy=fallback_strategy)
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def parse_open_positions(file_bytes) -> 'pd.DataFrame | None':
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"""Parse the Open Positions section from MT5 account history HTML."""
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import pandas as pd
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text = _decode(file_bytes)
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rows = re.findall(r'<tr[^>]*>(.*?)</tr>', text, re.DOTALL)
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in_open = False
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in_orders = False
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positions = []
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for row in rows:
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cells = re.findall(r'<t[dh][^>]*>(.*?)</t[dh]>', row, re.DOTALL)
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cells = [re.sub(r'\s+', ' ', _strip(c)).strip() for c in cells]
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cells = [c for c in cells if c]
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if not cells:
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continue
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flat = ' '.join(cells)
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if 'Open Positions' in flat:
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in_open = True
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in_orders = False
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continue
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if 'Working Orders' in flat or 'Pending Orders' in flat:
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in_orders = True
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in_open = False
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continue
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if 'Results' in flat or 'Closed Positions' in flat or 'Balance:' in flat:
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if in_open or in_orders:
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break
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# Header row
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if cells[0] in ('Time', 'Open Time'):
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continue
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# Open position row: Time, Position, Symbol, Type, Volume, Price, SL, TP
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# followed sometimes by a profit row (fewer cols)
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if in_open and len(cells) >= 6 and re.match(r'\d{4}\.', cells[0]):
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try:
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vol_str = cells[4].split('/')[0].strip()
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positions.append({
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'open_time' : _to_dt(cells[0]),
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'position' : cells[1],
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'symbol' : cells[2],
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'type' : cells[3].lower(),
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'volume' : _to_float(vol_str),
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'open_price' : _to_float(cells[5]),
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'sl' : _to_float(cells[6]) if len(cells) > 6 else None,
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'tp' : _to_float(cells[7]) if len(cells) > 7 else None,
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'status' : 'open',
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})
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except Exception:
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pass
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if not positions:
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return None
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df = pd.DataFrame(positions)
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df['symbol_base'] = df['symbol'].str.replace(r'\.[a-z]+$', '', regex=True).str.upper()
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return df
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# ── Auto-detect format ────────────────────────────────────────────────────────
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@@ -297,9 +394,13 @@ def detect_and_parse(file_bytes, filename=''):
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Returns (df, format_name) or (None, None).
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"""
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fname = filename.lower()
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# Derive a fallback strategy name from the filename for files where
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# all comments are MT5 close-reason tags (sl/tp/so) and no strategy
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# name is embedded in the comment field.
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fallback = _strategy_from_filename(filename) if filename else None
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if fname.endswith('.csv'):
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df = parse_quant_csv(file_bytes)
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df = parse_quant_csv(file_bytes, fallback_strategy=fallback)
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return df, 'Quant Analyzer CSV'
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# HTML/HTM — detect backtest vs real account
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@@ -309,16 +410,16 @@ def detect_and_parse(file_bytes, filename=''):
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return None, None
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if 'Strategy Tester Report' in text or 'strategy tester' in text.lower():
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df = parse_backtest_report(file_bytes)
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df = parse_backtest_report(file_bytes, fallback_strategy=fallback)
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return df, 'MT5 Backtest Report'
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df = parse_mt5_report(file_bytes)
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df = parse_mt5_report(file_bytes, fallback_strategy=fallback)
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return df, 'MT5 Account History'
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# ── Stats ─────────────────────────────────────────────────────────────────────
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def calc_stats(df):
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def calc_stats(df, deposit=0.0):
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if df is None or len(df) == 0:
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return {}
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@@ -340,8 +441,15 @@ def calc_stats(df):
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max_cl = _max_consec(results, False)
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cumulative = df.sort_values('close_time')['net_profit'].cumsum()
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rolling_max = cumulative.cummax()
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max_dd = round((cumulative - rolling_max).min(), 2)
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# Balance series: deposit + cumulative P&L (matches MT5 Balance Drawdown Maximal)
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balance = deposit + cumulative
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rolling_max = balance.cummax()
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drawdown_ser = balance - rolling_max
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max_dd = round(drawdown_ser.min(), 2)
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peak_equity = round(rolling_max.max(), 2)
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# % = max_dd / local peak at the point of max drawdown
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peak_at_dd = rolling_max.loc[drawdown_ser.idxmin()] if not drawdown_ser.empty else peak_equity
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max_dd_pct = round(max_dd / peak_at_dd * 100, 2) if peak_at_dd != 0 else 0
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avg_dur = round(df['duration_min'].mean(), 1) if 'duration_min' in df.columns else 0
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avg_win_dur = round(wins['duration_min'].mean(), 1) if len(wins) > 0 else 0
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@@ -369,6 +477,8 @@ def calc_stats(df):
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'max_consec_wins' : max_cw,
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'max_consec_losses' : max_cl,
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'max_drawdown' : max_dd,
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'max_drawdown_pct' : max_dd_pct,
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'peak_equity' : peak_equity,
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'best_trade' : round(df['net_profit'].max(), 2),
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'worst_trade' : round(df['net_profit'].min(), 2),
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'avg_duration_min' : avg_dur,
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@@ -382,14 +492,13 @@ def calc_stats(df):
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def extract_strategy(comment):
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if not comment or str(comment).strip() == '':
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if not comment or str(comment).strip() in ('', 'nan'):
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return 'Manual'
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parts = str(comment).split('_')
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while parts and re.match(r'^\d+$', parts[-1]):
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parts.pop()
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if parts and re.match(r'^[A-Z]{3,8}(\.a)?$', parts[-1]):
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parts.pop()
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return '_'.join(parts) if parts else str(comment)
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s = str(comment).strip()
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# Filter out MT5 close-reason comments: "sl 1234.56", "tp 1234.56", "so 50%"
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if re.match(r'^(sl|tp|so)\s+[\d\.]+%?$', s, re.IGNORECASE):
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return 'Manual'
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return s
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def _max_consec(results, target):
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