Zero2Ai

Trading Journal

数据分析v1.0.0
作者:Zero2Ai更新于 2026-05-11skill-trading-journal@clawhub_zero2ai-hub/skill-trading-journal
报表生成数据洞察#Trading Strategy

据 AlphaMoat 统计,截至 2026-09,Trading Journal 累计下载 3,969 次、获 2 个星标,在全部 5,078 个 AI 技能中排名第 2,592 位,在「数据分析」分类(399 个)中排名第 199 位。

累计安装
123
累计下载
3,969
星标
2
全站排名
#2,592
全站前 52% · 「数据分析」前 50%
平台累计安装累计下载星标评论
ClawHub ↗1233,9692-
SkillHub ↗1193,7502—

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

主文给出本地 JSON 日志路径与记录结构,要求新交易包含论点、止损、止盈、仓位规则和催化因素;周报与月度复盘用于识别模式并提出策略调整。交易记录和建议可能影响决策,且日志含敏感财务信息,应保护本地文件并在采取任何策略调整前自行审核。

资料发布于 2026-10-29

可以用来做什么

  • 记录交易的进场依据和退出结果
  • 整理每周或每月的交易复盘
  • 检查交易记录是否包含规则要求的字段

什么时候用

  • 用户明确要求整理自己的交易日志或复盘已有记录时

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

先给出一条交易记录所需字段清单,不访问或修改本地日志。
汇总复盘报告中需要核验的损益、胜率和策略建议,不提出买卖指令。

月度数据趋势

累计下载3,969
1,3942,7774,1602026-03 · 累计下载 1,5852026-05 · 累计下载 2,8662026-06 · 累计下载 3,0862026-07 · 累计下载 3,5992026-08 · 累计下载 3,8422026-09 · 累计下载 3,96926-0326-09
星标2
1222026-03 · 星标 12026-05 · 星标 22026-06 · 星标 22026-07 · 星标 22026-08 · 星标 22026-09 · 星标 226-0326-09

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。

月份累计下载环比星标
2026-093,969—2
2026-083,842+6.8%2
2026-073,599+16.6%2
2026-063,086+7.7%2
2026-052,866—2
2026-031,585—1

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。

3.7 KB · ClawHub v1.0.0
---
name: skill-trading-journal
description: Log every trade with full context (thesis, entry, exit, PnL, emotion, lesson). Generate weekly and monthly performance reports. Identify patterns in wins/losses. Use when recording a new trade, reviewing performance, running a weekly debrief, or updating the trading strategy based on results.
---

# Trading Journal

Systematic trade logging and performance review. Every trade logged = strategy gets smarter over time.

## Trade Log Storage
展开全部(共 126 行)

Stored at: `~/.openclaw/workspace/trading/journal.json`

## Log a New Trade

```
Log trade to trading/journal.json:
- Symbol: GRASSUSDT
- Direction: LONG
- Entry: $0.36
- Entry date: 2026-03-13
- Size: $50 USDC
- Thesis: Price broke above $0.30 threshold + GTC keynote catalyst in 3 days + volume rising
- Catalyst: NVIDIA GTC 2026
- Signal source: skill-crypto-threshold-watcher
- Stop loss: $0.28 (22% below entry)
- Take profit: $0.50 (39% above entry)
```

## Log Trade Exit

```
Update journal entry [trade-id] with exit:
- Exit price: $0.46
- Exit date: 2026-03-17
- Exit reason: Take profit not hit, manual exit (Aladdin decision)
- PnL: +$12.78 (+27.7%)
- Lesson: Entry thesis was correct. Exit was early — could have held to $0.50 TP.
```

## Trade Record Schema

```json
{
  "id": "trade-001",
  "symbol": "GRASSUSDT",
  "direction": "LONG",
  "status": "CLOSED",
  "entry_price": 0.36,
  "entry_date": "2026-03-13",
  "entry_size_usdc": 50,
  "thesis": "Price broke above $0.30 threshold + GTC keynote catalyst",
  "catalyst": "NVIDIA GTC 2026",
  "signal_source": "skill-crypto-threshold-watcher",
  "stop_loss": 0.28,
  "take_profit": 0.50,
  "exit_price": 0.46,
  "exit_date": "2026-03-17",
  "exit_reason": "Manual exit — pre-TP",
  "pnl_usdc": 12.78,
  "pnl_pct": 27.7,
  "lesson": "Exit was early. Hold to TP next time unless thesis breaks.",
  "emotion": "neutral",
  "thesis_correct": true,
  "execution_correct": false
}
```

## Weekly Performance Report

```
Generate weekly trading report from trading/journal.json for the past 7 days.
Include: total PnL, win rate, avg win vs avg loss, best/worst trade, lessons summary, strategy adjustments.
```

**Sample output:**
```
📊 WEEKLY TRADING REPORT — Mar 10–17, 2026

Trades: 2 | Wins: 2 | Losses: 0 | Win Rate: 100%
Total PnL: +$18.43 (+23.4% on deployed capital)
Avg Win: +$9.21 | Avg Loss: N/A
Best Trade: GRASSUSDT +$12.78 (+27.7%)

LESSONS THIS WEEK:
- Thesis-correct but execution left early on GRASS — hold to TP when thesis intact
- Volume spike threshold ($100M) on FET called the move correctly

STRATEGY ADJUSTMENTS:
- Raise FET volume threshold to $120M (reduce false positives)
- Add trailing stop at +20% to avoid leaving gains on table

NEXT WEEK CATALYSTS:
- [from catalyst-calendar]
```

## Monthly Strategy Review

```
Generate monthly strategy review from trading/journal.json.
Identify: which signal types worked, which failed, regime conditions, recommended rule updates.
```

## Integration with Trading Pipeline

- **Inputs from:** `skill-crypto-threshold-watcher` (signal), `skill-catalyst-calendar` (context), `binance-pro` (execution confirmation)
- **Outputs to:** `backtest-expert` (validated signals for re-testing), `quant-trading-system` (parameter updates)
- **Review cadence:** Weekly debrief every Sunday 20:00 UTC

## Emotion Tracking

Log emotion at trade entry: `calm | excited | fearful | greedy | neutral`

Over time, identify if emotional state correlates with win/loss rate. This is where most traders lose edge.

## Rules Enforcement

Before logging a new trade, verify:
- [ ] Thesis stated in one sentence
- [ ] Stop-loss defined
- [ ] Take-profit defined
- [ ] Position size within rulebook limits
- [ ] Catalyst identified (or "none — pure technical")

If any field missing → trade is NOT valid per rulebook.

「数据分析」分类下的热门技能

数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。