trade-journal

name trade-journal description Analyze a user's trade journal (CSV/Excel broker export). Parses 同花顺/东方财富/富途/generic formats, produces a trading profile and 4 behavior diagnostics (disposition effect, overtrading, chasing, anchoring). Use the `analyze_trade_journal` tool. category tool Trade Journal Analysis Purpose Users upload broker exports (交割单) and get an honest, data-grounded portrait of their own trading. Two layers are live: Profile — holding days, frequency, win rate, PnL ratio, cumulative PnL, max drawdown, top symbols, market/hourly distribution. Behavior diagnostics — 4 biases, each with severity (low/medium/high) and numeric evidence: disposition effect, overtrading, chasing momentum, anchoring. Strategy extraction → backtest bridge lands in Phase 4c. Supported formats (auto-detected): 同花顺 (Tonghuashun) — A-share CSV, typically GBK-encoded 东方财富 (Eastmoney) — A-share CSV, typically GBK-encoded 富途 (Futu) — HK/US CSV, UTF-8 Generic — any CSV with columns like datetime/symbol/side/qty/price Usage Call the analyze_trade_journal tool directly. Never run Python from bash. analyze_trade_journal(file_path="uploads/xxx.csv") analyze_trade_journal(file_path="uploads/xxx.csv", analysis_type="profile") analyze_trade_journal(file_path="uploads/xxx.csv", filter_expr="2026-01 to 2026-03") analyze_trade_journal(file_path="uploads/xxx.csv", filter_expr="symbol=600519.SH") analyze_trade_journal(file_path="uploads/xxx.csv", filter_expr="market=china_a") analysis_type : full (default) — profile + behavior (strategy still placeholder) profile — profile metrics only (fastest) behavior — 4 behavior diagnostics only strategy — Phase 4c placeholder filter_expr (optional): Date range: "YYYY-MM to YYYY-MM" or "YYYY-MM-DD to YYYY-MM-DD" Symbol: "symbol=600519.SH" (exact match on qualified symbol) Market: "market=china_a|us|hk|crypto" Return shape (profile subset) { "status" : "ok" , "file" : "xxx.csv" , "format_detected" : "tonghuashun" , "total_records" : 326 , "date_range" : "2026-01-06 ~ 2026-03-28" , "symbols_count" : 42 , "market" : "china_a" , "profile" : { "total_trades" : 326 , "total_roundtrips" : 118 , "avg_holding_days" : 3.2 , "trade_frequency_per_week" : 4.1 , "win_rate" : 0.48 , "profit_loss_ratio" : 1.35 , "total_pnl" : 18240.55 , "max_drawdown" : -9820.10 , "top_symbols" : [ { "symbol" : "600519.SH" , "trades" : 14 , "total_amount" : 1.02e6 } , ... ] , "market_distribution" : { "china_a" : 326 } , "hourly_distribution" : { 9 : 52 , 10 : 84 , ... } , "roundtrips_sample" : [ { "symbol" : "600519.SH" , "buy_dt" : "..." , "sell_dt" : "..." , "pnl" : 3400.1 , "pnl_pct" : 0.021 , "hold_days" : 2.5 } , ... ] } } Note: PnL uses FIFO lot matching; unmatched open positions are excluded from win rate / PnL ratio (only closed round-trips count). Presenting results to the user Produce a single markdown report in the user's language. Lead with the top-line numbers, then section-by-section. Keep it dense — this is retail readers skimming on a phone. Report template ## 你的交易画像 — {date_range} **总体** - 交易笔数：{total_trades}（完整来回 {total_roundtrips} 次） - 平均持仓：{avg_holding_days} 天 - 交易频率：{trade_frequency_per_week} 次/周 - 胜率：{win_rate:.0%} - 盈亏比：{profit_loss_ratio} - 累计盈亏：{total_pnl} - 最大回撤：{max_drawdown} **最常交易的标的**（前 5 名） | 标的 | 笔数 | 成交额 | |------|------|--------| | ... | ... | ... | **市场分布** {market_distribution} **交易时段** {hourly_distribution — highlight peak hours} **一句话观察** （根据数据写 1-2 句：过度交易？只做窄范围标的？集中在某时段？） Guidance: If win_rate < 0.4 AND profit_loss_ratio < 1.0 → explicit warning: losing on both win rate and payoff. Ask whether they want behavior diagnostics (Phase 4b) or a cooling-off reality check. If avg_holding_days < 1 AND trade_frequency_per_week > 15 → flag intraday-heavy pattern, note that minute-level backtest would be better. If symbols_count <= 3 → concentration risk; ask if they want a sector- diversification check. Follow-up dialogue After the initial report, users typically ask: Time-slice : "3 月份表现怎么样" → re-call with filter_expr="2026-03-01 to 2026-03-31" . Symbol deep-dive : "茅台这只赚了多少" → filter_expr="symbol=600519.SH" . Market split : "港股和美股分开看" → two calls, market=hk and market=us . Hypothetical ("如果我严格止损 -5%") → Phase 4b feature; for now tell the user this is on the roadmap. Do NOT re-upload — the file path is still valid for subsequent tool calls in the same session. Error handling File not found / Unsupported extension — ask user to re-upload. Unrecognized trade journal format — share the detected columns back to the user and ask them to rename the key columns to: datetime, symbol, side, quantity, price, amount, fee (generic fallback). No trade records parsed — likely empty file or header-only; ask user to confirm the export contains actual fills. Behavior diagnostics (shape) Under result["behavior"] : { "disposition_effect" : { "severity" : "high" , "ratio_loss_to_win_hold" : 1.69 , "avg_winner_hold_days" : 7.4 , "avg_loser_hold_days" : 12.5 , "evidence" : "Losing roundtrips held 12.5d vs winning 7.4d (ratio 1.69). Classic disposition pattern." } , "overtrading" : { "severity" : "high" , "busy_day_avg_pnl" : -2632 , "quiet_day_avg_pnl" : 759 , "evidence" : "On busy days (≥3 trades) avg PnL -2632; on quiet days (≤1) avg PnL +759. High activity hurts returns." } , "chasing_momentum" : { "severity" : "medium" , "chase_ratio" : 0.5 , "buys_evaluated" : 4 , "evidence" : "2/4 buys (50%) came after a >3% price run-up in the same symbol. Some chasing tendency." } , "anchoring" : { "severity" : "high" , "anchored_symbol_ratio" : 0.83 , "symbols_evaluated" : 6 , "anchored_symbols" : [ ... ] , "evidence" : "5/6 frequently-traded symbols stayed in a narrow price band (CV<5%). Strong anchoring." } } Detection logic (for user-facing explanation) Bias Metric Medium High Disposition effect avg_loser_hold / avg_winner_hold ≥ 1.2 ≥ 1.5 Overtrading (quiet − busy) / |quiet| day-PnL gap ≥ 0.3 ≥ 1.0 Chasing fraction of buys after 3-trade rolling +3% move ≥ 40% ≥ 60% Anchoring fraction of ≥5-trade symbols with price CV < 5% ≥ 33% ≥ 66% Report section (Chinese) ## 行为偏差诊断 | 偏差 | 严重程度 | 核心证据 | |------|----------|----------| | 处置效应 | {high/medium/low} | {evidence} | | 过度交易 | {...} | {...} | | 追涨杀跌 | {...} | {...} | | 锚定效应 | {...} | {...} | **改进建议**（根据检测到的 high/medium 项生成）： - 处置效应 high → 写死止损（例如 -8%），盈利持仓不要过早兑现 - 过度交易 high → 每日交易次数 <= N 的硬约束 - 追涨杀跌 high → 改买回调而不是新高，设置"涨幅 X% 以上当日不追"规则 - 锚定效应 high → 扩宽价格带，不要死守某个"心理价" Phase 4c preview (not yet implemented) Strategy extraction → SignalEngine code gen → auto-backtest lands in Phase 4c. When the user asks for it, respond honestly and offer the behavior diagnostics instead (they're live).