algo-research-newsletter launch kit

One-liner

$49/mo Substack where every issue ships a backtested strategy with the actual vectorbt code, last-week's paper-trade P&L, and next-week's setup. Not vibes.

Buyer

Retail quants, indie traders, prop-shop juniors who want strategy ideas delivered weekly without doing all the data engineering themselves.

Pain

• Most "trading newsletters" sell narrative + tickers without backtests
• vectorbt + yfinance is free but eats hours/week to run consistently
• Wallstreetbets is noise; Quantocracy is research-paper-deep but not actionable; we sit in the middle

Differentiator

• Code shipped, not just claims
• Walk-forward results (in-sample → out-of-sample → paper) shown
• Token-frugal LLM use (no per-backtest LLM cost — Claude only writes the prose around already-computed numbers)
• Substack handles auth/billing/distribution

Disclaimer/risk framing

• "Not investment advice" disclaimer in every issue
• We publish OUR paper P&L weekly so readers can see whether the strategies hold up out-of-sample on real data feeds
• No "guaranteed return" claims; we frame as research-product, not signal-service

Distribution

• Substack's recommendation network (paid subs from other Substacks)
• Twitter (FinTwit) threads recapping the prior week's results
• Cross-post the free intro issue to /r/algotrading, /r/quant
• Hacker News Show HN — "I'm shipping a backtested-strategy newsletter with the actual code"

algo-research-newsletter — paid Substack with backtested trading strategies

Weekly Substack newsletter where each issue ships:

1. A strategy hypothesis (e.g. "post-earnings drift in small-cap healthcare")
2. The vectorbt backtest with code (Sharpe, max drawdown, hit-rate, trade-count)
3. The paper-trade results from the prior week's strategy on Alpaca
4. The next week's setup with entry/exit rules

Pricing

Disclaimer

Not investment advice. Backtests don't predict future returns. Paper trading is paper trading — losses on real capital deployment are entirely the reader's. We document our own paper-trade P&L for transparency.

Why this works

• 90% of "trading newsletters" sell vibes; this ships code + data
• Substack handles billing + delivery — we don't run our own auth
• Strategies repeat: post-earnings drift, vol-surface squeezes, momentum in small-caps. Newsletter cadence beats one-shot reports
• Substack network effects: paid subscribers refer

Distribution

• Twitter (FinTwit) threads recapping the prior week's paper P&L
• Cross-post free issues to /r/algotrading, /r/quant
• Bridge content on Hacker News

What goes in each issue

• Hypothesis (1-2 paragraphs)
• The backtest (Python notebook, vectorbt)
• Walk-forward results (in-sample vs out-of-sample)
• Risk profile (Sharpe, drawdown, hit-rate, trade count)
• Entry / exit rules in plain English
• Paper-trade execution log from prior week

How LLM helps (token-frugal)

Claude (cached system prompt) handles:

1. Weekly market-summary context
2. Translating the strategy code into plain-English entry/exit rules
3. Drafting the issue prose around the data

The actual backtests run locally in vectorbt. No LLM call per backtest. Cost per issue: ~$0.05 in API tokens.

Order

https://openclaw-revenue.vercel.app/products/algo-research-newsletter