FICC004: Daily FICC Brief Generator - 把 FI / FX / Commodities 合成每日研究工作流
FICC004: Daily FICC Brief Generator - 把 FI / FX / Commodities 合成每日研究工作流
这是 PENGYI_FICC_MAP 的 FICC004。
前面四篇已经把 FICC 的地基铺出来：
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FICC000 -> FICC 总地图
FICC001 -> Fixed Income / Rates / Credit
FICC002 -> Currencies / FX
FICC003 -> Commodities
FICC004 -> Daily FICC Brief Generator
这一篇开始从“知识学习”进入“系统搭建”。
目标不是直接预测市场，也不是直接生成交易信号。 目标是设计一个每日 FICC 研究简报生成器：
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Daily FICC Brief Generator
它把三条线合在一起：
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Fixed Income:
rates, yield curve, credit spread, funding, duration
Currencies / FX:
spot, forward, swap, rate differential, carry, basis, central bank divergence
Commodities:
energy, metals, agriculture, inventory, futures curve, geopolitics
然后每天输出一份结构化 brief：
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what happened
why it happened
which asset moved
which macro variable changed
which narrative is stronger
what evidence supports it
what counter-evidence exists
what to watch next
how yesterday's forecast performed
公开边界：
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This is educational material and research infrastructure thinking.
It is not investment advice, trading advice, or an actionable alpha note.
中文边界：
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这是公开学习笔记和研究基础设施设计，不是投资建议。
不包含内部数据、客户信息、未脱敏策略、实盘观点或可交易 alpha。
一句话总览
Daily FICC Brief Generator 的本质是：
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把每天分散在 rates、FX、commodities、macro data、central bank、news 和 market commentary 里的信息，转化成一份可追踪、可复盘、可审计的跨资产研究简报。
更工程化一点：
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Daily FICC Brief = data ingestion + event extraction + cross-asset mapping + evidence retrieval + narrative ranking + forecast ledger + human review.
它不是一个“自动喊方向”的 agent。 它更像一个研究副驾驶：
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research copilot
market memory
event ledger
cross-asset reasoning assistant
daily review system
为什么需要 Daily Brief
FICC 市场每天的信息密度非常高。
每天会有：
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macro data release
central bank speech
yield curve movement
credit spread widening / tightening
FX spot move
forward / basis change
oil inventory report
commodity curve move
geopolitical headline
risk sentiment shift
client / market commentary
如果不结构化，这些信息会变成碎片。
人脑会出现几个问题：
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1. 只记住最新 headline，忘掉之前的上下文。
2. 对某个叙事过度自信，忽略反例。
3. 看到了价格变化，但没拆清楚驱动来源。
4. 每天都在读新东西，但没有形成可复盘的研究资产。
5. 昨天的判断没有被系统记录，今天就无法真实校准。
Daily Brief Generator 要解决的就是这些问题。
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not more information
but better structure
中文：
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不是读更多信息，而是把信息整理成可复用的研究结构。
Daily FICC Brief 的核心输出
一份好的 Daily FICC Brief 不应该只是新闻摘要。
它应该包括：
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1. Market Snapshot
2. Macro Calendar
3. Rates and Credit
4. FX and Dollar Liquidity
5. Commodities
6. Cross-Asset Causal Chain
7. Key Narrative
8. Evidence and Counter-Evidence
9. Forecast Ledger Update
10. Watchlist and Human Review
也就是：
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市场发生了什么
宏观变量怎么变
资产价格怎么反应
跨资产链条是否一致
当前主叙事是什么
这个叙事有哪些证据
有哪些反例
之前判断是否被验证
明天重点看什么
哪些地方必须人工审核
这才是 research workflow。
输入层
Daily Brief 的输入可以分成六类：
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market data
macro data
policy text
news and events
research notes
forecast ledger
更具体：
| 输入 | 示例 | 作用 |
|---|---|---|
| Market data | yields, curves, FX spot, commodity futures, spreads | 记录价格和风险因子变化 |
| Macro data | CPI, payroll, GDP, PMI, trade balance, inventory | 识别宏观 surprise |
| Policy text | FOMC, ECB, BoJ, PBoC, speeches | 提取央行 stance |
| News and events | geopolitics, sanctions, supply disruption, election | 抽取事件冲击 |
| Research notes | public commentary, internal-safe notes, public reports | 提供解释和上下文 |
| Forecast ledger | yesterday’s claims and watchlist | 做复盘和校准 |
公开版本的数据边界应该清楚：
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public data
public documents
sanitized notes
no client information
no proprietary signal
no trading instruction
输出层
Daily Brief 的输出可以固定成一个模板：
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# Daily FICC Brief
Date:
Market Regime:
Main Narrative:
Confidence:
Human Review Required:
## 1. Executive Summary
## 2. Market Snapshot
## 3. Macro and Policy Calendar
## 4. Fixed Income: Rates / Credit / Funding
## 5. FX: USD / G10 / EM / Basis
## 6. Commodities: Energy / Metals / Agriculture
## 7. Cross-Asset Causal Chain
## 8. Evidence
## 9. Counter-Evidence
## 10. Forecast Ledger Review
## 11. Watchlist
## 12. Open Questions
它要同时服务三类读者：
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human researcher
portfolio manager
future agent memory
所以 brief 不能只写给人看。 也要适合机器读取。
可以同时输出：
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markdown report
json report
event table
claim table
forecast ledger update
系统架构
可以先设计成七层：
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Layer 1: Data Ingestion
Layer 2: Normalization
Layer 3: Event Extraction
Layer 4: Retrieval and Memory
Layer 5: Cross-Asset Reasoning
Layer 6: Brief Generation
Layer 7: Human Review and Ledger Update
完整链路：
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raw inputs
-> normalized observations
-> extracted events
-> linked entities and assets
-> retrieved historical context
-> generated narratives
-> evidence / counter-evidence check
-> forecast ledger update
-> human reviewed brief
核心原则：
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每一个结论都要能追溯到 observation / event / evidence。
也就是：
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no unsupported market narrative
Layer 1: Data Ingestion
输入层负责把每天的信息收进来。
典型输入：
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rates:
treasury yields
swap rates
yield curve slope
breakeven inflation
credit spreads
repo / funding indicators
FX:
major FX pairs
DXY
forward points
cross-currency basis
implied volatility
risk reversal
commodities:
oil futures
gas futures
gold
copper
grains
futures curve
inventory report
macro:
CPI
payroll
GDP
PMI
retail sales
trade balance
central bank meetings
text:
central bank statements
speeches
official reports
public news
public market summaries
第一版不需要接复杂数据源。 可以先用：
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CSV
manual markdown notes
public official links
small JSON fixtures
关键不是数据源豪华。 关键是结构要对。
Layer 2: Normalization
Normalization 把不同来源变成统一格式。
统一观察对象：
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Observation:
id
date
asset_class
asset
metric
value
change
unit
source
timestamp
例如：
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{
"date": "2026-07-03",
"asset_class": "rates",
"asset": "US10Y",
"metric": "yield",
"value": 4.32,
"change": 0.06,
"unit": "percent",
"source": "public_market_data"
}
统一事件对象：
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Event:
id
date
event_type
region
entities
summary
affected_assets
expected_direction
actual_market_reaction
source
统一 claim 对象：
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Claim:
id
date
claim_text
claim_type
assets
evidence_ids
counter_evidence_ids
confidence
horizon
status
这个很重要。 因为后面的 forecast ledger 和 human review 都依赖它。
Layer 3: Event Extraction
Event extraction 要从文本和数据里抽取“今天真正发生了什么”。
事件类型可以先定义成：
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macro_surprise
central_bank_signal
rate_move
curve_move
credit_spread_move
fx_move
commodity_supply_event
commodity_inventory_event
geopolitical_event
risk_sentiment_event
funding_stress_event
例如：
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US CPI higher than expected
Fed speech more hawkish than previous
2s10s curve steepened
USD broadly strengthened
Brent front spread moved into stronger backwardation
Gold rallied despite USD strength
好的事件抽取不是只写 headline。 它要写清：
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event
surprise
affected variable
market reaction
uncertainty
模板：
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Event:
What happened?
Was it expected?
Which variable changed?
Which asset reacted?
Was reaction consistent with theory?
What else could explain it?
Layer 4: Retrieval and Memory
Daily Brief 必须有记忆。
否则每天都像第一次看市场。
记忆层包括：
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previous daily briefs
forecast ledger
event history
asset-specific notes
central bank stance history
macro regime notes
cross-asset causal templates
RAG 检索的问题可以是：
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上一次 CPI surprise 之后 US10Y 怎么走？
过去三次 Fed hawkish surprise 对 DXY 的影响是什么？
oil inventory draw 对 Brent curve 的历史解释是什么？
gold 在 real yield 上升时为什么还会涨？
carry trade unwind 的历史模式有哪些？
这一步让 brief 从“新闻摘要”升级为“研究记忆”。
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retrieval turns today's headline into historical context.
中文：
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检索把今天的 headline 放回历史上下文。
Layer 5: Cross-Asset Reasoning
FICC 的价值在 cross-asset。
Daily Brief 最核心的推理是：
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macro event -> rates -> FX -> commodities -> credit -> risk sentiment
常见链条：
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inflation upside surprise
-> front-end yields up
-> real rates up
-> USD stronger
-> gold pressure
-> credit risk depends on growth implication
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oil supply shock
-> oil price up
-> headline inflation pressure
-> commodity exporter FX support
-> importer terms-of-trade pressure
-> central bank reaction may become more hawkish
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risk-off shock
-> equities down
-> credit spreads wider
-> safe haven demand
-> USD / JPY / CHF reaction
-> yields may fall if growth fear dominates
但系统必须允许“不一致”。
例如：
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USD up and gold up at the same time
这不是错误。 可能说明：
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safe haven demand dominates real-rate pressure
又例如：
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yields up but credit spreads also widen
可能说明：
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market is pricing inflation and financial condition tightening together
所以 cross-asset reasoning 不能只套公式。 它要列出多个机制，并给证据排序。
Narrative Ranking
每天市场都会有多个叙事。
例如：
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hawkish Fed
growth slowdown
oil supply risk
China demand weakness
USD funding stress
carry unwind
risk-on rebound
Daily Brief 要做的是 narrative ranking：
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哪一个叙事最能解释今天的资产联动？
哪一个叙事只是局部解释？
哪一个叙事和市场价格冲突？
哪一个叙事需要明天继续验证？
评分可以先简单：
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Narrative Score =
price confirmation
+ cross-asset consistency
+ source reliability
+ historical similarity
- counter-evidence
输出：
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Main Narrative:
Fed repricing dominated today's FICC market.
Supporting Evidence:
front-end yields rose
USD strengthened
gold weakened
rate-sensitive equities underperformed
Counter-Evidence:
credit spreads did not widen materially
oil move was driven by inventory headline
Watch:
next CPI and Fed speakers
这就是研究组织能力。
Evidence and Counter-Evidence
每个 claim 都必须绑定证据。
Claim 示例：
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Today's USD strength was mainly driven by rate differential rather than broad risk-off.
Evidence：
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US front-end yields rose more than peers.
EUR/USD and USD/JPY moved in direction consistent with rate spread widening.
Equity drawdown was limited, and credit spreads were stable.
Counter-evidence：
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Some safe-haven assets also rallied.
FX vol rose modestly.
Commodity FX weakened more than rates alone would suggest.
系统输出必须保持这种结构：
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claim
evidence
counter-evidence
confidence
what would change my mind
这也是面试和研究训练里最重要的能力。
Forecast Ledger
Daily Brief 必须带 forecast ledger。
否则每天写得再漂亮也不能进化。
Ledger 记录：
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date
claim
asset
horizon
expected direction
confidence
evidence
counter-evidence
actual outcome
review
lesson
示例：
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Claim:
If Fed speakers continue hawkish tone, front-end yields may remain supported this week.
Horizon:
1 week
Evidence:
stronger inflation surprise, repricing in front-end curve
Counter-evidence:
risk-off growth concern may pull yields lower
Review:
Pending
这里要注意：
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forecast 不等于 trading signal。
它可以只是研究假设：
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research hypothesis
scenario expectation
risk monitor
Forecast Ledger 的意义是：
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让研究判断可复盘，让模型和人都能校准。
Human Review
FICC brief 不能完全交给 agent 自动发布。
至少要有人审这几个点：
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1. 是否把相关性误写成因果？
2. 是否漏掉关键反例？
3. 是否输出了不该公开的 alpha 或内部信息？
4. 是否把教育材料写成了交易建议？
5. 是否对市场结论过度自信？
6. 是否存在数据源不可靠或时间戳错误？
7. 是否对事件理解错了国家、货币、合约或期限？
Human review checklist：
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Source checked?
Timestamp checked?
Asset direction checked?
Unit checked?
Claim supported?
Counter-evidence included?
No confidential data?
No trading instruction?
Confidence calibrated?
Next watch item clear?
这就是我们之前一直讲的：
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human PM review
它不是形式主义。 它是 Research OS 的安全阀和质量阀。
Brief 模板
下面是一份可复用模板。
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# Daily FICC Brief
Date:
Prepared by:
Review status:
## 1. Executive Summary
- Main narrative:
- Key market move:
- Cross-asset consistency:
- Biggest uncertainty:
- Human review notes:
## 2. Market Snapshot
| Asset | Latest | Change | Interpretation |
|---|---:|---:|---|
| US 2Y | | | |
| US 10Y | | | |
| 2s10s | | | |
| DXY | | | |
| EUR/USD | | | |
| USD/JPY | | | |
| Brent | | | |
| Gold | | | |
| Copper | | | |
| IG spread | | | |
| HY spread | | | |
## 3. Macro and Policy Calendar
- Today's releases:
- Surprise vs expectation:
- Central bank comments:
- Next key events:
## 4. Fixed Income
- Rates:
- Curve:
- Inflation expectation:
- Credit:
- Funding:
- Main FI interpretation:
## 5. FX
- USD:
- G10:
- EM:
- Forward / basis:
- Vol:
- Main FX interpretation:
## 6. Commodities
- Energy:
- Metals:
- Agriculture:
- Curve / inventory:
- Main commodities interpretation:
## 7. Cross-Asset Causal Chain
```text
event
-> macro variable
-> rates / FX / commodities reaction
-> risk sentiment
-> open question
```
## 8. Evidence
- Evidence 1:
- Evidence 2:
- Evidence 3:
## 9. Counter-Evidence
- Counter-evidence 1:
- Counter-evidence 2:
- Counter-evidence 3:
## 10. Forecast Ledger Review
| Previous Claim | Horizon | Outcome | Lesson |
|---|---|---|---|
| | | | |
## 11. New Research Hypotheses
| Claim | Horizon | Evidence | Counter-Evidence | Confidence |
|---|---|---|---|---|
| | | | | |
## 12. Watchlist
- Watch item 1:
- Watch item 2:
- Watch item 3:
JSON Schema
为了让机器能读，brief 还应该有 JSON 版。
第一版 schema 可以很简单：
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{
"date": "YYYY-MM-DD",
"main_narrative": "",
"market_regime": "",
"confidence": "low|medium|high",
"sections": {
"fixed_income": {
"summary": "",
"key_moves": [],
"claims": []
},
"fx": {
"summary": "",
"key_moves": [],
"claims": []
},
"commodities": {
"summary": "",
"key_moves": [],
"claims": []
}
},
"cross_asset_chains": [],
"evidence": [],
"counter_evidence": [],
"forecast_ledger_updates": [],
"watchlist": [],
"human_review": {
"required": true,
"status": "pending",
"notes": []
}
}
后续可以升级成：
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Pydantic models
SQLite storage
DuckDB analytics
vector database
graph database
static markdown publishing
但第一版不需要过度工程化。
Agent Workflow
可以把 Daily Brief Generator 拆成多个 agent。
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Data Agent:
收集 market data / macro data / text inputs
Event Agent:
抽取今天发生的事件
Rates Agent:
分析 rates / curve / credit / funding
FX Agent:
分析 spot / forward / basis / USD / carry
Commodities Agent:
分析 energy / metals / agriculture / inventory / curve
Cross-Asset Agent:
生成跨资产因果链
Skeptic Agent:
找反例、找不一致、降低过度自信
Writer Agent:
生成 markdown / json brief
Reviewer:
人类审核并批准发布
注意这里的 agent 不应该互相“聊天到失控”。 应该由 workflow 控制：
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fixed inputs
fixed outputs
schema validation
evidence required
counter-evidence required
human review required
这就是 harness 的意义。
Harness 设计
Daily FICC Brief 的 harness 可以定义为：
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task
input schema
tool permissions
retrieval scope
output schema
validation rules
review rules
logging rules
例如：
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Task:
Generate daily FICC brief from public-safe inputs.
Allowed tools:
local markdown notes
approved public data
approved RAG index
forecast ledger
Not allowed:
client data
confidential documents
live trading orders
private alpha database
Validation:
every claim needs evidence
every narrative needs counter-evidence
every forecast needs horizon
all trading advice language removed
human review before publish
这直接连接我们的 PENGYI_HARNESS_MAP。
RAG 设计
RAG 的 corpus 可以分几类：
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FICC knowledge notes:
FICC000 - FICC003
official documents:
central bank statements
EIA reports
WASDE reports
macro releases
market memory:
previous daily briefs
forecast ledger
event history
public research:
sanitized public notes
educational materials
检索策略：
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query by asset
query by event
query by macro variable
query by regime
query by historical analogy
例子：
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Query:
oil price up and USD up, historical interpretation
Retrieve:
commodity inflation notes
USD liquidity notes
previous oil shock briefs
FICC003 oil / inflation section
FICC002 USD / commodity FX section
RAG 的目标不是替代判断。 目标是给判断提供上下文和证据。
Graph 设计
Graph 可以让 cross-asset 更清晰。
核心节点：
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Asset
Currency
Country
CentralBank
MacroVariable
Commodity
Curve
Event
Claim
Evidence
Forecast
核心关系：
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CentralBank -> sets -> PolicyRate
PolicyRate -> affects -> YieldCurve
YieldCurve -> affects -> FXForward
Commodity -> affects -> Inflation
Inflation -> affects -> CentralBankReaction
USD -> affects -> CommodityPrice
CommodityShock -> affects -> TermsOfTrade
TermsOfTrade -> affects -> Currency
Event -> supports -> Claim
Evidence -> supports -> Claim
CounterEvidence -> challenges -> Claim
Forecast -> reviews -> Claim
这样 brief 可以回答：
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今天这个事件影响了哪些节点？
哪些资产反应一致？
哪些资产反应冲突？
哪些旧 claim 被支持？
哪些旧 claim 被挑战？
这就是 Graph RAG 在 FICC 里的实际落点。
Evaluation
Daily Brief Generator 也需要评估。
不能只看文字漂不漂亮。
可以评估：
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coverage
factuality
source grounding
cross-asset consistency
counter-evidence quality
forecast calibration
human edit distance
time saved
review pass rate
具体指标：
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Coverage:
是否覆盖 FI / FX / Commodities 三块
Grounding:
关键 claim 是否有来源或 observation 支持
Consistency:
跨资产因果链是否自洽
Skepticism:
是否主动列出反例
Calibration:
预测 confidence 是否和后续结果匹配
Human Review:
人类需要改多少
第一版可以做人工评分：
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1 - poor
2 - acceptable
3 - good
4 - very good
5 - publishable
每天评一次。 这就是持续进化。
MVP 怎么做
第一版 MVP 不要复杂。
目标：
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用本地 markdown + CSV + JSON 生成一份 daily brief。
文件结构：
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ficc_daily_brief/
data/
market_snapshot.csv
macro_calendar.csv
events.md
ledger/
forecast_ledger.csv
notes/
ficc000.md
ficc001.md
ficc002.md
ficc003.md
outputs/
daily_ficc_brief_YYYY-MM-DD.md
daily_ficc_brief_YYYY-MM-DD.json
src/
ingest.py
extract_events.py
generate_brief.py
validate_brief.py
MVP 流程：
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1. 人工填一份 market_snapshot.csv。
2. 人工写当天 events.md。
3. 系统读取 FICC 笔记和 ledger。
4. 生成 markdown brief。
5. 生成 json brief。
6. validate 检查 claim / evidence / counter-evidence。
7. 人类审核。
8. 写入 forecast ledger。
这个版本已经足够展示能力：
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domain understanding
research workflow
agent harness
structured output
human review
forecast ledger
public-safe finance AI
Pseudocode
第一版伪代码：
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def run_daily_ficc_brief(date: str):
market = load_market_snapshot(date)
macro = load_macro_calendar(date)
events = load_event_notes(date)
ledger = load_forecast_ledger()
memory = load_ficc_notes()
observations = normalize_market_data(market)
extracted_events = extract_events(events, macro)
rates_view = analyze_rates(observations, extracted_events, memory)
fx_view = analyze_fx(observations, extracted_events, memory)
commodities_view = analyze_commodities(observations, extracted_events, memory)
chains = build_cross_asset_chains(
rates_view=rates_view,
fx_view=fx_view,
commodities_view=commodities_view,
events=extracted_events,
)
claims = rank_narratives(chains)
claims = attach_evidence_and_counter_evidence(claims, memory, observations)
brief = render_markdown_brief(
date=date,
observations=observations,
events=extracted_events,
rates_view=rates_view,
fx_view=fx_view,
commodities_view=commodities_view,
claims=claims,
ledger=ledger,
)
validate_brief(brief)
save_outputs(brief)
return brief
这里真正重要的是接口。
每个模块都应该输出结构化对象，而不是自由文本乱飞。
面试怎么讲
如果被问：
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你说你做 FICC AI Research OS，具体是什么？
可以回答：
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我会先做 Daily FICC Brief Generator。
它不是自动交易系统，而是研究工作流系统。输入包括公开 market data、macro calendar、central bank text、official reports、news event notes 和 forecast ledger；系统把这些信息标准化成 observations、events、claims 和 evidence，然后分别生成 rates、FX、commodities 三条线的分析，再通过 cross-asset reasoning 合成主叙事、反例和下一步 watchlist。最后必须经过 human review，并把新研究假设写入 forecast ledger，方便后续复盘和校准。
如果被问：
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这个和普通新闻摘要有什么区别？
可以回答：
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普通摘要只是压缩文本。Daily FICC Brief Generator 要做的是研究结构化：每个 market narrative 都要绑定 evidence 和 counter-evidence；每个观点要放进 FI、FX、Commodities 的跨资产链条里；每个 forecast 都有 horizon、confidence 和后续 review。这使它可以被审计、复盘和持续改进。
如果被问：
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为什么要 human review？
可以回答：
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金融研究里错误的因果解释和过度自信很危险。Human review 用来检查数据源、时间戳、因果链、反例、保密边界和语言边界，确保输出是 public-safe research note，而不是未经验证的交易建议。
和 Quant Research OS 的连接
Daily FICC Brief 是 Quant Research OS 的上游。
它输出：
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events
narratives
hypotheses
watchlist
forecast outcomes
这些可以进入：
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event study
factor hypothesis generation
regime classification
risk dashboard
macro feature library
strategy research queue
例如：
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brief detects:
oil inventory shock + curve backwardation strengthens + commodity FX reacts
research queue creates:
event study on inventory surprise and oil curve
commodity FX sensitivity analysis
inflation expectation follow-through study
这就是：
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research brief -> structured hypothesis -> quant experiment -> forecast review
我们最终要做的是闭环。
和 Credit OS 的连接
这套系统也能服务个人 credit。
因为它可以公开展示：
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金融市场理解
AI agent workflow
RAG / Graph / Harness 思维
工程化能力
研究复盘能力
public-safe writing
对外呈现时，不要展示内部 alpha。 展示的是：
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how I think
how I structure research
how I build systems
how I control risk
how I review evidence
这就是 public credit。
下一步
FICC004 完成后，后面可以继续：
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FICC005:
FICC Event-to-Signal Workflow
FICC006:
FICC RAG / Graph RAG System
FICC007:
FICC Forecast Ledger and Evaluation
FICC008:
FICC Interview Playbook
如果要进入工程落地，最建议下一步是：
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FICC005 -> Event-to-Signal Workflow
不是直接实盘。 而是把事件、假设、验证和复盘流程打通。
当前结论
Daily FICC Brief Generator 是 FICC 系列从知识到系统的第一步。
它把：
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Fixed Income
FX
Commodities
Macro
News
RAG
Graph
Forecast Ledger
Human Review
合成一个每日研究工作流。
核心不是“让 AI 预测市场”。 核心是：
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让研究过程结构化、可追踪、可复盘、可审计、可持续进化。
一句话收束：
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Daily FICC Brief Generator turns market noise into structured research memory.
中文：
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Daily FICC Brief Generator 把市场噪音变成结构化研究记忆。
This post is licensed under CC BY 4.0 by the author.