Bilingual Wealth Creation Blueprint

From Credit Risk to Alpha: Unlocking U.S. and China Markets with AI, Trading, and Historical Insights

从信用风险到阿尔法：利用AI、交易和历史洞察解锁美国与中国市场

Your Edge: A Unique Combination of Expertise

With 20 years in credit risk modeling, a Ph.D. in Economics, investment and trading expertise (Macro-Technical Confluence Model, sentiment analysis), history and early education skills, and bilingual Chinese/English fluency with 25 years in the U.S., you deliver data-driven, culturally nuanced solutions at 10x speed using AI.

凭借20年信用风险建模经验、经济学背景、投资与交易专长（宏观-技术融合模型、情绪分析）、历史与早期教育技能以及25年美国居住经验的双语能力，您能够利用AI以10倍速度提供数据驱动、具有文化深度的解决方案。

Tagline: “From Basel III to Buffett—quantifying risk, capturing alpha, powered by 5,000 years of history.”

口号： “从巴塞尔协议到巴菲特——量化风险，捕捉阿尔法，源自5000年历史的力量。”

Model 1: Quant-Historian Alpha Generator (Content + Tools)

Positioning

“Where Credit Risk, Trading, and Historical Insights Meet AI to Deliver Alpha”

“信用风险、交易与历史洞察结合AI，创造阿尔法”

Audience: Retail traders, hedge funds, finance educators, Chinese-American investors, Chinese HNWIs.

Target Niches

U.S. → China: “Credit-Driven Trading Strategies for Chinese Immigrants” – Trading ETFs (SPY) with credit signals, 401(k) strategies, risk management.
China → U.S.: “U.S. Markets for Chinese Investors” – Options (iron condors), forex (USD/CNY), macro drivers (Fed vs. PBOC).
Hybrid: “Bilingual Financial Literacy for Families” – Teaching kids investing, college savings (529 plans vs. Chinese funds).

目标市场：
- 美国→中国：“为中国移民提供信用驱动的交易策略” – 交易ETF（SPY）与信用信号、401(k)策略、风险管理。
- 中国→美国：“美国市场为中国投资者” – 期权（铁鹰策略）、外汇（美元/人民币）、宏观驱动（美联储 vs. 中国央行）。
- 混合：“双语家庭金融素养” – 教授儿童投资、大学储蓄（529计划 vs. 中国基金）。

AI-Powered Workflow

Content Creation: Use Claude 3.5 to draft articles; DeepL for Chinese translation, refined with trading and credit risk insights. (~2 hours/article)
Data Insights: Python (pandas, yfinance, backtrader) for backtesting (e.g., SPY momentum with HYG spreads). (~2 hours/backtest)
Repurposing: YouTube Shorts/LinkedIn (EN), Xiaohongshu/WeChat Video (CN) using Descript and Canva. (~1 hour/platform)
Visuals: Leonardo.ai for culturally resonant imagery (e.g., Chinese-American traders). (~30 min/image set)

Monetization

Affiliates: Interactive Brokers ($100/referral), TradingView ($30/referral), Tiger Brokers ($50/referral). Revenue: $2,000-$5,000/mo (6 months).
Premium Discord: $500/mo for trade alerts + credit risk models. Revenue: $5,000-$10,000/mo (10-20 subs).
Sponsorships: Bloomberg, Futu ($2,000-$5,000/post, 20k followers).
E-books: “Backtesting 2008 Crash with AI” ($49, Gumroad). Revenue: $1,500-$3,000/mo (3 months).

60-Day Action Plan

Week 1: Launch “Dr. [Your Name]’s Credit & Markets” on LinkedIn/微信公众号. Post: “Why 2025 = 1994 (Trade It).”
Week 2-3: Free lead magnet: “5 AI-Backtested Trading Signals.” Collect emails (Beehiiv).
Week 4-5: 2 bilingual articles + YouTube Shorts/WeChat Videos (e.g., “USD/CNY Trading”).
Week 6-8: Launch $49 e-book (Gumroad). Drive 50 sales ($2,450).

Start Model 1 Today

Model 2: Credit-Alpha AI Advisory Firm

Positioning

“Credit Risk, Trading, and Macro Insights for Institutional and High-Net-Worth Clients”

“为机构和高净值客户提供信用风险、交易和宏观洞察”

Audience: Hedge funds, family offices, Chinese SMEs, U.S. firms entering China.

Core Services

Service	Description	AI + Expertise	Pricing
Portfolio Armoring	Optimize portfolios with credit risk models	Python (scikit-learn) for PD/LGD models; macro signals (Fed hikes)	$30,000/quarter
Tactical Asset Allocation	Dynamic trading strategies	Backtrader for momentum strategies; historical analogs (1997 crisis)	1% AUM ($50,000-$100,000/year)
China-U.S. Risk Arbitrage	Exploit credit/market inefficiencies	Scrape CBIRC/FRED data; Claude 3.5 for 2008 analogs	$50,000/project
Bilingual Financial Training	Workshops for cross-border teams	ChatGPT slide decks; early education techniques	$3,000/workshop

Client Acquisition

U.S.: LinkedIn posts with charts (e.g., “HYG Spreads vs. 2008”). DM 20 prospects/week. Goal: 1 client ($30,000/quarter).
China: Pitch in WeChat groups (e.g., “Shanghai Investors”) as “美国信用与交易专家”. Goal: 1 client ($20,000/project).

60-Day Action Plan

Week 1: Mock risk arbitrage report (NPL vs. HYG Spreads).
Week 2-3: Outreach to 30 U.S. funds + 30 Chinese family offices.
Week 4-5: Close 1 client ($30,000/quarter) with pilot project.
Week 6-8: Deliver pilot, upsell $3,000 workshop.

Start Model 2 Today

Model 3: Algorithmic Trading & Education Products

Positioning

“Scalable, AI-Driven Trading and Financial Education Tools”

“可扩展的AI驱动交易与金融教育工具”

Audience: Retail traders, trading academies, bilingual families, Chinese HNWIs.

Product Ideas

Product	Description	AI + Expertise	Pricing
Crisis Navigator SaaS	Real-time trading signals	Python (Streamlit, yfinance) for spreads/sentiment; 1929/2008 analogs	$199/mo, $10,000/year (API)
Warren Buffett Meets AI Simulator	Historical trading modules	Gretel.ai for synthetic data; backtest 1987 crash	$299/course
Family Office in a Box	Credit-risk-aware allocation	Python (pandas) for VaR; historical filters	$5,000/year
Bilingual Trading Guide	“U.S. Options vs. China Futures”	Claude 3.5; 2015 China crash analogs	$149

Sales Channels

U.S.: Gumroad, Teachable, QuantConnect (strategy packs).
China: 淘宝 Taobao, Xiaohongshu Store, 得到 Dedao (courses).

60-Day Action Plan

Week 1: “Crisis Navigator” MVP ($199/mo, Streamlit).
Week 2-3: $149 bilingual guide (Gumroad/Xiaohongshu).
Week 4-5: 3-lesson trading course ($99, Teachable/Dedao).
Week 6-8: 4 Xiaohongshu/LinkedIn posts with charts. Drive 50 sales ($5,000-$7,000).

Start Model 3 Today

Nuclear Combo: Credit-to-Trading Signal Engine

Process

Data Input: Scrape Chinese NPLs (CBIRC) + U.S. HYG flows (FRED).
AI Analysis: Claude 3.5 for historical analogs (1997/2008); Python (TA-Lib) for momentum screens.
Outputs:
- Institutional Reports: $15,000/quarter.
- Retail Alerts: $99/mo (Discord).
- K-12 Modules: $199/course (“Crises for Kids”).

Edge: Your credit risk, trading, historical, and bilingual expertise creates defensible IP.

优势：您的信用风险、交易、历史和双语专长打造了不可复制的知识产权。

AI + Data Tool Stack

Task	Tool	Your Supercharge
Backtesting	QuantConnect, Backtrader	Credit-risk constraints
Signal Generation	Python (TA-Lib), Claude 3.5	NPL ratio forecasts
Synthetic Data	Gretel.ai	1970s stagflation simulation
Client Reports	ChatGPT Advanced Data Analysis	Auto-embedded FRED charts + history

90-Day Roadmap & Next Steps

Roadmap

Month 1: Launch Model 1. Achieve $3,000-$5,000 in affiliate/product revenue.
Month 2: Scale to Model 2. Secure 1-2 clients ($30,000-$50,000/quarter).
Month 3: Expand to Model 3. Sell 100 products ($10,000-$15,000) or close 3 clients ($50,000-$100,000).

Pricing Power Playbook

Content: Charge 3x competitors ($49 vs. $149 for guides).
Advisory: Value-based fees (1% of protected assets).
Products: Tiered pricing ($99 retail, $10,000 enterprise).

Today’s Next Step

Launch Model 1. Use Claude 3.5 to draft a bilingual article: “How Chinese Investors Can Trade U.S. Markets with Credit Risk Signals.” Back with yfinance data (SPY vs. HYG spreads, 2024) and 1994 crash analog. Post on LinkedIn (EN) and Xiaohongshu (CN) by July 20, 2025.

启动Model 1。使用Claude 3.5起草双语文章：“中国投资者如何利用信用风险信号交易美国市场。” 以yfinance数据（2024年SPY vs. HYG价差）和1994年崩盘类比为支撑。在LinkedIn（英文）和小红书（中文）上发布，截止2025年7月20日。

Get Started Now