Quant Trading for Programmers 26: Abstract Notification Channels First
Article 26 adds a paper-trading notification-channel abstraction, stabilizing the send interface and receipt structure before connecting files, Lark, email, or other channels.
Blog Archive
Page 6. Posts are ordered by date, with each page loading a bounded set of covers.
Quant Trading for Programmers 25: Add Production Checks To Paper Trading
Article 25 adds paper-trading production checks for cash, market prices, target weights, daily reports, and review records, then reviews articles 21-25.
Quant Trading for Programmers 24: Persist Paper-Trading Account State
Article 24 saves and restores paper-trading account state with JSON files, persisting only cash and positions instead of derived snapshots so the daily flow can continue after restarts.
Quant Trading for Programmers 23: Link The Daily Paper-Trading Flow
Article 23 links account snapshots, risk checks, rebalance plans, recommendation summaries, daily alert messages, and review records into one testable paper-trading daily cycle.
Quant Trading for Programmers 22: Save Daily Paper-Trading Review Records
Article 22 adds paper-trading review records, saving daily equity, cash ratio, risk severity, and recommendation action as sortable entries with multi-day summaries.
Quant Trading for Programmers 21: Compress Paper-Trading Results Into A Recommendation Summary
Article 21 combines account snapshots, risk reports, and rebalance plans into HOLD, REBALANCE, and REDUCE_RISK actions so paper-trading output is easier to review and alert on.
Codex Practice Notes: Modes, Spark, Skills, Slash Commands, And exec
A practical Q&A about Codex collaboration modes, GPT-5.3-Codex-Spark, statusline configuration, skills, slash commands, codex exec, and the Codex SDK.
Quant Trading for Programmers 20: Generate Paper-Trading Daily Reports And Alert Summaries
Article 20 combines account snapshots, risk reports, and rebalance plans into paper-trading daily report text, leaving a stable message format for Lark, email, or other notification channels.
Quant Trading for Programmers 19: Generate Rebalance Plans From Target Weights
Article 19 implements the first rebalance-plan layer, converting gaps between current and target weights into A-share board-lot buy and sell suggestions without modifying the account.
Quant Trading for Programmers 18: Paper Trading Needs Risk Checks Too
Article 18 implements paper-trading risk checks based on account snapshots, validating total exposure, cash buffer, and single-stock weight with readable violations.
Quant Trading for Programmers 17: Generate Paper-Trading Account Snapshots
Article 17 builds account snapshots on top of the paper-trading ledger, calculating cash ratio, position market value, floating PnL, and weights as unified input for risk controls and notifications.
Quant Trading for Programmers 16: Start With A Clear Paper-Trading Ledger
Article 16 enters paper trading by adding accounts, positions, and execution results, making buy, sell, cash, and market-value updates testable ledger logic.
Quant Trading for Programmers 15: Strategy Promotion Needs A Gate
Article 15 implements a strategy promotion gate: candidates must satisfy minimum return, max drawdown, trade-count, and baseline-comparison requirements before entering paper-trading observation.
How I Use AI for Real Engineering Work: PRs, Docs, Remote Ops, and Releases
A sanitized field note on using Codex, Claude Code, Cursor, gh, and lark-cli for real engineering tasks: what work is worth delegating to AI, what boundaries must stay firm, and how to make AI-assisted work verifiable and shippable.
Quant Trading for Programmers 14: Save Strategy Candidates As Experiment Records
Article 14 implements strategy experiment records by saving parameter-search candidates, baseline results, metric deltas, status, and decisions into structured payloads for later review.
Quant Trading for Programmers 13: Add A First Grid Search For Strategy Parameters
Article 13 implements parameter search by generating short-window, long-window, and position-ratio candidates, running one backtest per group, and ranking results with return, drawdown, and turnover penalties.
Quant Trading for Programmers 12: Final Return Is Not Enough
Article 12 adds reusable backtest metrics: total return, annualized return, annualized volatility, Sharpe-like ratio, max drawdown, win rate, profit-loss ratio, turnover, and trade count.
Quant Trading for Programmers 11: From One Stock To A Portfolio Backtest
Article 11 extends the first single-symbol backtest into a multi-symbol portfolio backtest with equal-weight capital allocation, per-symbol runs, equity aggregation, and trade-count summaries.