Feature:
- added IC markets trade export file to trade analysis with account filter option
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"""
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icmarkets_parser.py
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===================
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Parser for IC Markets MT5 Position History XLSX exports.
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The file contains one sheet ("MT5 Position history List") with:
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- Row 0: "Report"
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- Row 1: Name / Produced At metadata
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- Row 2: Column headers
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- Row 3+: Deal rows (one row per leg — In or Out)
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Each closed trade has two legs sharing the same Position ID:
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- "Trade Buy In" / "Trade Sell In" → entry leg (Profit = 0)
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- "Trade Buy Out" / "Trade Sell Out" → exit leg (Profit = actual P/L)
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Open positions have only an In leg (no Out yet).
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Usage
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-----
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from icmarkets_parser import parse_icmarkets_xlsx, get_icmarkets_accounts
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# Get list of accounts in the file
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accounts = get_icmarkets_accounts(file_bytes)
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# → ['11586098', '11586099']
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# Parse all accounts (returns combined DataFrame)
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df = parse_icmarkets_xlsx(file_bytes)
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# Parse a specific account
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df = parse_icmarkets_xlsx(file_bytes, account="11586098")
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Output columns (normalised to match mt5_parser schema)
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-------------------------------------------------------
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symbol, type, open_time, close_time, open_price, close_price,
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volume, net_profit, win, commission, swap, comment,
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_account, _strategy, position_id, position_status,
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open_date, close_date, day_of_week, hour, duration_min,
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symbol_base
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"""
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import io
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import pandas as pd
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import numpy as np
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# ─────────────────────────────────────────────────────────────────────────────
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# Public helpers
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# ─────────────────────────────────────────────────────────────────────────────
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def get_icmarkets_accounts(file_bytes: bytes) -> list[str]:
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"""Return sorted list of account numbers found in the file."""
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raw = _read_raw(file_bytes)
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if raw is None:
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return []
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accounts = raw["Account Number"].dropna().unique().tolist()
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return sorted([str(a) for a in accounts if str(a).strip()])
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def parse_icmarkets_xlsx(file_bytes: bytes, account: str = None) -> pd.DataFrame:
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"""
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Parse IC Markets position history XLSX into a normalised trade DataFrame.
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Parameters
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----------
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file_bytes : bytes
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Raw bytes of the .xlsx file.
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account : str, optional
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If provided, only trades for this account number are returned.
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If None, all accounts are combined.
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Returns
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-------
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pd.DataFrame with normalised columns ready for use in mt5_parser-compatible
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analysis pages.
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"""
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raw = _read_raw(file_bytes)
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if raw is None or raw.empty:
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return pd.DataFrame()
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# Filter by account if requested
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if account:
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raw = raw[raw["Account Number"].astype(str) == str(account)].copy()
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if raw.empty:
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return pd.DataFrame()
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# ── Separate In (entry) and Out (exit) legs ────────────────────────────
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ins = raw[raw["Transaction Type"].str.contains("In", na=False)].copy()
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outs = raw[raw["Transaction Type"].str.contains("Out", na=False)].copy()
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for df in (ins, outs):
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df["Position"] = pd.to_numeric(df["Position"], errors="coerce")
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# ── Pair by Position ID ────────────────────────────────────────────────
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paired = ins.merge(
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outs[["Position", "Date Time", "Open Price", "Profit",
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"Transaction Type"]].rename(columns={
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"Date Time": "Date Time_close",
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"Open Price": "Close Price",
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"Profit": "Profit_close",
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"Transaction Type": "TT_close",
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}),
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on="Position",
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how="left", # keep open positions too (no Out leg yet)
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)
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# ── Build normalised columns ───────────────────────────────────────────
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out = pd.DataFrame()
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out["position_id"] = paired["Position"]
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out["symbol"] = paired["Symbol"].astype(str)
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out["symbol_base"] = out["symbol"].str.split(".").str[0]
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out["_account"] = paired["Account Number"].astype(str)
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out["position_status"] = paired["Position Status"].astype(str)
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# Direction from the In leg transaction type
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out["type"] = paired["Transaction Type"].apply(
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lambda x: "buy" if "Buy" in str(x) else "sell"
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)
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out["open_time"] = pd.to_datetime(paired["Date Time"], errors="coerce")
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out["close_time"] = pd.to_datetime(paired["Date Time_close"], errors="coerce")
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out["open_price"] = pd.to_numeric(paired["Open Price"], errors="coerce")
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out["close_price"] = pd.to_numeric(paired["Close Price"], errors="coerce")
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out["volume"] = pd.to_numeric(paired["Trade Volume Lots"],errors="coerce")
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out["net_profit"] = pd.to_numeric(paired["Profit_close"], errors="coerce").fillna(0)
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out["commission"] = 0.0 # IC Markets file doesn't separate commission
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out["swap"] = 0.0
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out["comment"] = ""
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out["_strategy"] = out["symbol_base"] # use symbol as strategy label
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# ── Derived columns ────────────────────────────────────────────────────
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out["win"] = out["net_profit"] > 0
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out["open_date"] = out["open_time"].dt.date
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out["close_date"] = out["close_time"].dt.date
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out["day_of_week"] = out["open_time"].dt.day_name()
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out["hour"] = out["open_time"].dt.hour
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out["duration_min"] = ((out["close_time"] - out["open_time"])
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.dt.total_seconds() / 60).round(1)
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return out.reset_index(drop=True)
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# ─────────────────────────────────────────────────────────────────────────────
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# Internal helpers
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# ─────────────────────────────────────────────────────────────────────────────
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def _read_raw(file_bytes: bytes) -> pd.DataFrame | None:
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"""Read the raw sheet and return a DataFrame with named columns."""
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try:
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buf = io.BytesIO(file_bytes)
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df = pd.read_excel(buf, sheet_name=0, header=None, dtype=str)
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except Exception:
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return None
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# Find the header row — it contains "Symbol" in column 0
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header_row = None
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for i, row in df.iterrows():
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if str(row.iloc[0]).strip() == "Symbol":
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header_row = i
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break
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if header_row is None:
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return None
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df.columns = df.iloc[header_row].tolist()
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df = df.iloc[header_row + 1:].copy()
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df.columns.name = None
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# Keep only real data rows (Position Status = Open or Closed)
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if "Position Status" in df.columns:
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df = df[df["Position Status"].isin(["Open", "Closed"])].copy()
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return df.reset_index(drop=True)
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+117
-22
@@ -11,37 +11,132 @@ sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from mt5_parser import detect_and_parse, calc_stats
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def _normalise_ic(df):
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"""Map IC Markets DataFrame columns to the schema expected by calc_stats."""
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import pandas as pd
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out = df.copy()
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# calc_stats / render helpers need: open_time, close_time, symbol, type,
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# strategy, net_profit, win, volume, open_price, close_price,
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# commission, swap, profit, duration_min, day_of_week, hour
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if "symbol_base" in out.columns and "strategy" not in out.columns:
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out["strategy"] = out["symbol_base"]
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if "net_profit" in out.columns and "profit" not in out.columns:
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out["profit"] = out["net_profit"]
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if "commission" not in out.columns:
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out["commission"] = 0.0
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if "swap" not in out.columns:
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out["swap"] = 0.0
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if "sl" not in out.columns:
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out["sl"] = None
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if "tp" not in out.columns:
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out["tp"] = None
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# Ensure win column
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if "win" not in out.columns and "net_profit" in out.columns:
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out["win"] = out["net_profit"] > 0
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# Ensure day_of_week and hour
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if "open_time" in out.columns:
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out["open_time"] = pd.to_datetime(out["open_time"], errors="coerce")
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if "day_of_week" not in out.columns:
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out["day_of_week"] = out["open_time"].dt.day_name()
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if "hour" not in out.columns:
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out["hour"] = out["open_time"].dt.hour
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if "close_time" in out.columns:
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out["close_time"] = pd.to_datetime(out["close_time"], errors="coerce")
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if "duration_min" not in out.columns and "open_time" in out.columns and "close_time" in out.columns:
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out["duration_min"] = ((out["close_time"] - out["open_time"])
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.dt.total_seconds() / 60).round(1)
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return out
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def render():
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st.title("📊 Trade Analysis")
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# ── Session state ─────────────────────────────────────────────────────────
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if 'ta_df' not in st.session_state:
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st.session_state['ta_df'] = None
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st.session_state['ta_format'] = None
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for _k, _v in {
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'ta_df': None, 'ta_format': None,
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'ta_accounts': [], 'ta_ic_bytes': None,
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}.items():
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if _k not in st.session_state:
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st.session_state[_k] = _v
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# ── File upload ───────────────────────────────────────────────────────────
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col1, col2 = st.columns([4, 1])
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with col1:
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uploaded = st.file_uploader(
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"Upload MT5 Report (HTM/HTML) or Quant Analyzer CSV",
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type=['html', 'htm', 'csv'],
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key='ta_upload'
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# ── Source selector ──────────────────────────────────────────────────────
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src_col1, src_col2 = st.columns([4, 1])
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with src_col1:
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source = st.radio(
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"File source",
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["MT5 / Quant Analyzer", "IC Markets XLSX"],
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horizontal=True, key='ta_source',
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)
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with col2:
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with src_col2:
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st.markdown("<br>", unsafe_allow_html=True)
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if st.button("🗑 Clear", key='ta_clear'):
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st.session_state['ta_df'] = None
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st.session_state['ta_format'] = None
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st.session_state['ta_df'] = None
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st.session_state['ta_format'] = None
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st.session_state['ta_accounts'] = []
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st.rerun()
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if uploaded:
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df, fmt = detect_and_parse(uploaded.read(), uploaded.name)
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if df is not None:
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st.session_state['ta_df'] = df
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st.session_state['ta_format'] = fmt
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st.success(f"✓ Loaded {len(df)} trades — {fmt}")
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else:
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st.error("Could not parse report — check file format")
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# ── File upload ───────────────────────────────────────────────────────────
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if source == "MT5 / Quant Analyzer":
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uploaded = st.file_uploader(
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"Upload MT5 Report (HTM/HTML) or Quant Analyzer CSV",
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type=None, key='ta_upload',
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)
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if uploaded and uploaded.name.lower().endswith(('.htm','.html','.csv')):
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df, fmt = detect_and_parse(uploaded.read(), uploaded.name)
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if df is not None:
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st.session_state['ta_df'] = df
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st.session_state['ta_format'] = fmt
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st.session_state['ta_accounts'] = []
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st.success(f"✓ Loaded {len(df)} trades — {fmt}")
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else:
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st.error("Could not parse report — check file format")
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elif uploaded:
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st.warning("Please upload a .htm, .html, or .csv file.")
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else: # IC Markets XLSX
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uploaded = st.file_uploader(
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"Upload IC Markets Position History (.xlsx)",
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type=None, key='ta_upload',
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)
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if uploaded and uploaded.name.lower().endswith(('.xlsx','.xls')):
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try:
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from icmarkets_parser import get_icmarkets_accounts, parse_icmarkets_xlsx
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except ImportError as e:
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st.error(f"icmarkets_parser.py not found — ensure it is in the MT5Tools folder. ({e})")
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uploaded = None
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if uploaded:
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try:
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file_bytes = uploaded.read()
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accounts = get_icmarkets_accounts(file_bytes)
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if not accounts:
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st.error("No accounts found — check this is an IC Markets Position History export.")
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else:
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st.session_state['ta_ic_bytes'] = file_bytes
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st.session_state['ta_accounts'] = accounts
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st.session_state['ta_format'] = "IC Markets XLSX"
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df_ic = parse_icmarkets_xlsx(file_bytes, account=accounts[0])
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df_ic = _normalise_ic(df_ic)
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st.session_state['ta_df'] = df_ic
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st.success(f"✓ Loaded {len(df_ic)} trades — {len(accounts)} account(s) found")
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except Exception as e:
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st.error(f"Error parsing file: {e}")
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import traceback; st.code(traceback.format_exc())
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elif uploaded:
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st.warning("Please upload an .xlsx file.")
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# IC Markets account selector (shown after upload)
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if (st.session_state.get('ta_accounts') and
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st.session_state.get('ta_source', source) == "IC Markets XLSX"):
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accounts = st.session_state['ta_accounts']
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ac_opts = ["All accounts"] + accounts
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sel_ac = st.selectbox("Account", ac_opts, key='ta_ic_account')
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acct = None if sel_ac == "All accounts" else sel_ac
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if st.session_state.get('ta_ic_bytes'):
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from icmarkets_parser import parse_icmarkets_xlsx
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df_ic = parse_icmarkets_xlsx(st.session_state['ta_ic_bytes'], account=acct)
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df_ic = _normalise_ic(df_ic)
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st.session_state['ta_df'] = df_ic
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df_all = st.session_state['ta_df']
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fmt = st.session_state['ta_format']
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@@ -330,4 +425,4 @@ def render():
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data = df[show_cols].to_csv(index=False),
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file_name = f"mt5_trades_{date_from}_{date_to}.csv",
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mime = 'text/csv'
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)
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)
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