# I Asked Claude Code to Find Me a Trading Edge. It Killed Three Strategies First. > Source: > Published: 2026-08-14 07:26:03+00:00 *How I built a fully automated, rule-based Japanese stock research pipeline with Claude Code, J-Quants, and a macOS cron job — and why "the AI is not allowed to predict anything" turned out to be the most useful constraint in the whole project.* Everyone's first idea for "AI + stocks" is the same: ask the model whether a stock will go up. That idea has been tested, and it fails — LLMs guessing post-event price direction land around coin-flip accuracy. I'm a freelance web developer in Japan, not a quant, and I knew that if I let an LLM "predict" anything I would just be laundering my own wishful thinking through a chatbot. So I gave Claude Code a different job description. In my project's `CLAUDE.md` — the standing instructions file the agent reads every session — there's a hard rule: No predictions. Data only.The AI's role is limited to four things: (1) structuring data, (2) computing factual metrics, (3) checking facts against pre-defined rules, (4) explaining results with sources. Trading decisions are rule-based. Overfitting to past data is a hidden prediction — prefer economically sensible rules. Claude Code's job was to be the engineer: build the data pipeline, implement backtests I specified, and then — this is the important part — **kill my ideas with evidence**. Over a few weeks it killed three of them. Here's the honest record. Nothing exotic. The point was cheap and reproducible: `yfinance` for live tracking.First I had Claude Code implement the classics on Nikkei-universe data: breakout entries, RSI mean-reversion, with realistic costs and taxes included. Ten-year portfolio result: **+15.4%** total. Sounds okay until you put it next to the benchmark: buy-and-holding the Nikkei over the same period returned **+138.8%** (with a −26% max drawdown). My "strategy" wasn't a strategy; it was an expensive way to sit out a bull market. Verdict: dead. Next. Post-earnings announcement drift is one of the best-documented anomalies: stocks that beat guidance keep drifting up for weeks. On large caps, my backtest found nothing — no monotonicity across surprise sizes, slightly negative drift everywhere. Institutional money has eaten that edge. Small caps looked genuinely exciting: 276 stocks, 1,385 earnings events, 423 trades. The threshold sweep was beautifully monotonic — bigger guidance beats, bigger drift: +1.6% → +2.1% → +2.7% → +3.4% per trade as the threshold rose. Then I asked Claude Code to add one boring, adult filter: **only keep stocks with at least ¥100M in daily trading value** — i.e., stocks I could actually buy without moving the price. The edge evaporated: **−0.34%** per trade on the 33 surviving trades. At ¥300M/day it got worse (−2.48%). The anomaly was real, but it lives exclusively in stocks too illiquid to trade at size. This was the single most valuable chart the pipeline ever produced, and it's a *negative* result. If your backtest doesn't include an executability filter, it isn't a backtest — it's fan fiction. Third idea: a quarterly-rebalanced screen. Rank small caps by valuation (with quality guards: equity ratio ≥ 25%, positive forecast EPS, and a filter against one-off earnings spikes), split into quintiles, hold the cheapest. This one behaved differently. The cheapest quintile (Q1) returned **+8.89% per quarter**, returns were fully monotonic down the ranks, the cheap-vs-expensive spread was +8.96% per quarter, and Q1 beat the universe average in **all six** backtest quarters. And the crucial difference from PEAD: **the edge survived the liquidity filter.** Restricted to ≥¥100M/day stocks, Q1 still returned +10.06% per quarter (+78.7% cumulative vs +42.2% for the universe). The sign didn't flip. One strategy out of three survived its own audit. That's the pipeline working as intended. A backtest that survives in-sample is still just a hypothesis, so the surviving strategy went into "Phase 0": a paper portfolio tracked automatically every trading day, with a pre-committed bar to clear — beat the benchmark over 2–3 months *without touching the parameters* — before real money scales beyond pocket change. The automation is deliberately low-tech. A launchd job fires at 15:45 JST on weekdays (after the Tokyo close) and runs a shell script: ``` bash #!/bin/zsh # Runs from launchd on weekdays at 15:45 JST. # 1) Record any not-yet-entered positions at today's opening price # 2) Append the daily report to the log, push a summary to macOS notifications REPO="$HOME/work/makemoney" PY="$REPO/.venv/bin/python" { echo "===== $(date +%F) =====" "$PY" phase0_track.py open --date "$(date +%F)" report=$("$PY" phase0_track.py report) echo "$report" } >> "$REPO/data/phase0_daily.log" summary=$(echo "$report" | grep "Portfolio" | head -1) osascript -e "display notification \"$summary\" with title \"Phase 0\"" ``` The Python side is a small CLI with two subcommands. `open` records entries at the day's opening price (same conditions as the fractional-share "opening auction" orders I'd use with real money) with an idempotency guard, so re-running never double-records: ``` # double-entry guard existing = {p["ticker"] for p in journal.open_positions() if p["strategy"] == STRATEGY} lst = lst[~lst["ticker"].isin(existing)] ``` `report` marks the portfolio to market and compares it against two benchmarks (Nikkei 225 and a TOPIX ETF) *measured from the same entry date* — the comparison that Round 1 taught me never to skip. The result lands in a log file and a macOS notification. Total infrastructure cost: ¥0. As of 2026-08-14, one month after entry (18 positions, a small paper portfolio): Beating the Nikkei, narrowly losing to TOPIX. Inside the portfolio the dispersion is exactly what a small-cap quintile bet looks like: the best position is +46%, the worst is −33%. The daily report ends with a line I wrote for my own discipline: *"The pass bar is 2–3 months against the benchmark. Do not react to daily noise."* Maybe it clears the bar in October. Maybe it doesn't and the strategy joins the graveyard with the other three. Either outcome is the system working. A few workflow notes for anyone trying something similar: `CLAUDE.md` , not in your willpower.*Nothing here is investment advice — it's a build log of a personal research tool, running on pocket-money stakes precisely because the evidence isn't in yet. J-Quants data is used under its personal-use license.*