weekly-performance-digest

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Weekly Performance Digest

每周绩效摘要

Overview

概述

Weekly Performance Digest aggregates the trades you closed during a week into a single performance report. It reads CLOSED theses tracked by

trader-memory-core

(

state/theses/th_*.yaml

), computes headline metrics (win rate, expectancy, profit factor, R-multiple, MAE/MFE), breaks results down across several pattern dimensions (source skill, exit reason, thesis type, sector, mechanism tag, screening grade), and surfaces the week's biggest winners, losers, and lessons. Output is a JSON record plus a human-readable Markdown report. Pure calculation — no API key required.

每周绩效摘要将您一周内平仓的交易汇总成一份绩效报告。它读取由

trader-memory-core

追踪的已平仓交易记录（

state/theses/th_*.yaml

），计算核心指标（胜率、期望收益、利润因子、R-multiple、MAE/MFE），从多个维度（来源skill、退出原因、交易类型（thesis type）、行业、机制标签、筛选等级）拆分结果，并呈现本周的最大盈利、最大亏损及经验教训。输出为JSON记录和易读的Markdown报告。纯计算操作——无需API密钥。

When to Use

使用场景

At the end of a trading week to review aggregate realized performance
To measure win rate and expectancy across all closed positions
To see which source skills, exit reasons, sectors, or mechanisms drove wins vs losses
To feed a month-end review (combine four weekly digests) or a postmortem
For a quick "what worked / what didn't" snapshot grounded in real closed trades

交易周结束时，复盘整体已实现绩效
衡量所有平仓头寸的胜率和期望收益
查看哪些来源skill、退出原因、行业或机制主导了盈利与亏损
为月末复盘提供数据（合并四份周度摘要）或用于事后分析
获取基于真实平仓交易的快速“有效/无效策略”快照

When Not to Use

不适用场景

For a single-trade deep review — use
```
trade-performance-coach
```
For signal-level true/false-positive classification — use
```
signal-postmortem
```
For buy/sell recommendations or position sizing — this skill is descriptive only

单交易深度复盘——使用
```
trade-performance-coach
```
信号层面的真假阳性分类——使用
```
signal-postmortem
```
买卖建议或头寸规模计算——本skill仅作描述性分析

Prerequisites

前置条件

Python 3.9+ with
```
PyYAML
```
(already a repo dependency)
A
```
trader-memory-core
```
state directory of thesis YAML files (
```
state/theses/
```
)
No API key required

Python 3.9+ 及
```
PyYAML
```
（已作为仓库依赖项）
包含交易记录YAML文件的
```
trader-memory-core
```
状态目录（
```
state/theses/
```
）
无需API密钥

Workflow

工作流程

Step 1: Run the digest for a week

步骤1：生成某一周的绩效摘要

bash

python3 skills/weekly-performance-digest/scripts/generate_weekly_digest.py \
  --state-dir state/theses \
  --from-date 2026-06-13 --to-date 2026-06-20 \
  --output-dir reports/ -v

Defaults:

--state-dir state/theses

--from-date

= 7 days before

--to-date

--to-date

= today,

--output-dir reports/

. With no date flags it digests the trailing 7 days.

bash

python3 skills/weekly-performance-digest/scripts/generate_weekly_digest.py \
  --state-dir state/theses \
  --from-date 2026-06-13 --to-date 2026-06-20 \
  --output-dir reports/ -v

默认参数：

--state-dir state/theses

，

--from-date

--to-date

前7天，

--to-date

= 今日，

--output-dir reports/

。若未指定日期参数，则默认分析过去7天的数据。

Step 2: Read the report

步骤2：查看报告

The run writes

reports/weekly_digest_<to-date>.json

and

reports/weekly_digest_<to-date>.md

. Review the Markdown for the executive summary, metrics table, pattern breakdowns, and top winners/losers; consume the JSON downstream.

运行后会生成

reports/weekly_digest_<to-date>.json

和

reports/weekly_digest_<to-date>.md

。查看Markdown报告获取执行摘要、指标表格、模式细分及最大盈利/亏损记录；JSON文件可用于下游处理。

Step 3 (optional): Feed downstream

步骤3（可选）：下游处理

Combine several weekly JSON digests for a monthly review, or pass the JSON to a postmortem/coach step. The skill is descriptive — act on its findings via your normal review process.

合并多份周度JSON摘要用于月度复盘，或将JSON文件传入事后分析/指导步骤。本skill仅作描述性分析——需通过您的常规复盘流程根据分析结果采取行动。

How It Works

工作原理

Trade selection. A trade counts in a week if its
```
exit.actual_date
```
falls in
```
[from-date, to-date]
```
and
```
status == CLOSED
```
.

Win/loss.

outcome.pnl_dollars > 0

is a winner,

< 0

a loser,

== 0

breakeven;

win_rate = winners / total_trades

R-multiple.

pnl_dollars / ((entry.actual_price − exit.stop_loss) × position.shares)

. (Stop-loss is read from

exit.stop_loss

, per the real thesis schema.)

Double-counting safeguard. A CLOSED thesis's
```
outcome.pnl_dollars
```
is the cumulative realized P&L across all trims plus the final leg. Headline metrics use that cumulative value over CLOSED theses only. The separate
```
partial_trims
```
block scans
```
status_history[]
```
of PARTIALLY_CLOSED theses only (still open) and is reported for information — it is never added into the headline totals/win-rate. A position trimmed in week 1 then closed in week 2 therefore shows as a partial trim in week 1 and inside week 2's CLOSED headline; that is intended, not a duplicate.

交易筛选：若交易的
```
exit.actual_date
```
落在
```
[from-date, to-date]
```
区间且
```
status == CLOSED
```
，则计入当周统计。

盈亏判定：

outcome.pnl_dollars > 0

为盈利，

< 0

为亏损，

== 0

为盈亏平衡；

win_rate = 盈利交易数 / 总交易数

。

R-multiple：

pnl_dollars / ((entry.actual_price − exit.stop_loss) × position.shares)

。（止损价取自

exit.stop_loss

，遵循真实交易记录 schema。）

重复计数防护：已平仓交易记录的
```
outcome.pnl_dollars
```
是所有分批平仓加上最终平仓的累计已实现盈亏。核心指标仅使用已平仓交易记录的该累计值。单独的
```
partial_trims
```
模块仅扫描**PARTIALLY_CLOSED（仍持仓）**交易记录的
```
status_history[]
```
，仅作信息展示——绝不会计入核心指标的总数/胜率。因此，第1周分批平仓、第2周全部平仓的头寸会在第1周显示为分批平仓记录，在第2周计入核心指标；此为预期设计，并非重复计数。

Output Format

输出格式

JSON (

weekly_digest_<to-date>.json

)

JSON（

weekly_digest_<to-date>.json

）

json

{
  "schema_version": "1.0",
  "report_type": "weekly_performance_digest",
  "period": {"from": "2026-06-13", "to": "2026-06-20"},
  "generated_at": "2026-06-20T21:39:07Z",
  "summary": {
    "total_trades": 2, "winners": 1, "losers": 1, "breakeven": 0,
    "win_rate": 0.5, "expectancy": 25.0, "profit_factor": 2.0,
    "total_realized_pnl": 50.0, "total_realized_pnl_pct": 4.17
  },
  "metrics": {
    "avg_winner": 100.0, "avg_loser": -50.0,
    "largest_winner": 100.0, "largest_loser": -50.0,
    "avg_holding_days_winners": 9.0, "avg_holding_days_losers": 6.0,
    "r_multiple_avg": 0.25, "r_multiple_stdev": 1.06,
    "avg_mae_pct": -3.75, "avg_mfe_pct": 4.5
  },
  "pattern_analysis": {
    "by_source_skill": {"...": {"wins": 1, "losses": 0, "total": 1, "win_rate": 1.0}},
    "by_exit_reason": {}, "by_thesis_type": {}, "by_sector": {},
    "by_mechanism_tag": {}, "by_screening_grade": {}
  },
  "partial_trims": {"count": 0, "total_realized_pnl": 0.0, "trims": []},
  "lessons": {"top_wins": [], "top_losses": [], "process_improvements": []}
}

json

{
  "schema_version": "1.0",
  "report_type": "weekly_performance_digest",
  "period": {"from": "2026-06-13", "to": "2026-06-20"},
  "generated_at": "2026-06-20T21:39:07Z",
  "summary": {
    "total_trades": 2, "winners": 1, "losers": 1, "breakeven": 0,
    "win_rate": 0.5, "expectancy": 25.0, "profit_factor": 2.0,
    "total_realized_pnl": 50.0, "total_realized_pnl_pct": 4.17
  },
  "metrics": {
    "avg_winner": 100.0, "avg_loser": -50.0,
    "largest_winner": 100.0, "largest_loser": -50.0,
    "avg_holding_days_winners": 9.0, "avg_holding_days_losers": 6.0,
    "r_multiple_avg": 0.25, "r_multiple_stdev": 1.06,
    "avg_mae_pct": -3.75, "avg_mfe_pct": 4.5
  },
  "pattern_analysis": {
    "by_source_skill": {"...": {"wins": 1, "losses": 0, "total": 1, "win_rate": 1.0}},
    "by_exit_reason": {}, "by_thesis_type": {}, "by_sector": {},
    "by_mechanism_tag": {}, "by_screening_grade": {}
  },
  "partial_trims": {"count": 0, "total_realized_pnl": 0.0, "trims": []},
  "lessons": {"top_wins": [], "top_losses": [], "process_improvements": []}
}

Markdown (

weekly_digest_<to-date>.md

)

Markdown（

weekly_digest_<to-date>.md

）

Sections:

# Weekly Performance Digest

## Executive Summary

## Performance Metrics

## Pattern Analysis

## Lessons Learned

(

### Top Winners

### Top Losers

### Process Improvements

An empty week still produces a valid report with zeroed metrics (exit code 0).

包含章节：

# 每周绩效摘要

、

## 执行摘要

、

## 绩效指标

、

## 模式分析

、

## 经验教训

（

### 最大盈利

### 最大亏损

### 流程改进建议

）。

空交易周仍会生成指标全为0的有效报告（退出码为0）。

Resources

资源

```
scripts/generate_weekly_digest.py
```
— digest generator (JSON + Markdown)
```
references/weekly-digest-metrics.md
```
— metric formulas and interpretation

```
scripts/generate_weekly_digest.py
```
—— 摘要生成器（JSON + Markdown）
```
references/weekly-digest-metrics.md
```
—— 指标公式及解读

Key Principles

核心原则

Closed trades only for headline numbers — cumulative
```
outcome.*
```
, keyed on exit date.
No double-counting — partial trims are informational and excluded from totals.
Pattern attribution — every win/loss is attributed across multiple dimensions.
Descriptive, not prescriptive — the digest reports; you decide.

核心指标仅统计已平仓交易——累计
```
outcome.*
```
，按平仓日期统计。
无重复计数——分批平仓记录仅作信息展示，不计入总数。
模式归因——每笔盈亏均从多个维度进行归因分析。
仅作描述，不提供指令——摘要仅作报告，决策由您自行做出。

weekly-performance-digest

Original

Translation

Weekly Performance Digest

每周绩效摘要

Overview

概述

When to Use

使用场景

When Not to Use

不适用场景

Prerequisites

前置条件

Workflow

工作流程

Step 1: Run the digest for a week

步骤1：生成某一周的绩效摘要

Step 2: Read the report

步骤2：查看报告

Step 3 (optional): Feed downstream

步骤3（可选）：下游处理

How It Works

工作原理

Output Format

输出格式

JSON (
`weekly_digest_<to-date>.json`
)

JSON（
`weekly_digest_<to-date>.json`
）

Markdown (
`weekly_digest_<to-date>.md`
)

Markdown（
`weekly_digest_<to-date>.md`
）

Resources

资源

Key Principles

核心原则

weekly-performance-digest

Original

Translation

Weekly Performance Digest

每周绩效摘要

Overview

概述

When to Use

使用场景

When Not to Use

不适用场景

Prerequisites

前置条件

Workflow

工作流程

Step 1: Run the digest for a week

步骤1：生成某一周的绩效摘要

Step 2: Read the report

步骤2：查看报告

Step 3 (optional): Feed downstream

步骤3（可选）：下游处理

How It Works

工作原理

Output Format

输出格式

JSON (weekly_digest_<to-date>.json)

JSON（weekly_digest_<to-date>.json）

Markdown (weekly_digest_<to-date>.md)

Markdown（weekly_digest_<to-date>.md）

Resources

资源

Key Principles

核心原则

JSON (
`weekly_digest_<to-date>.json`
)

JSON（
`weekly_digest_<to-date>.json`
）

Markdown (
`weekly_digest_<to-date>.md`
)

Markdown（
`weekly_digest_<to-date>.md`
）