写一份《纪律.md》，代替我入市投资

Summary

This 面基 episode turns Trend Following into 可编程交易纪律: choose the tradable universe, define entry and exit states, set position limits, then separate signals and execution from opinion and emotion. It extends No-Prediction Trading by treating repeated small losses as the cost of reaching a few fat-tail winners, and it sharpens Quantitative Overfitting by preferring simple cross-asset rules over historically optimized parameters. A second branch introduces 注意力资产, a proposed product frame for tracking marginal changes in public attention rather than only follower totals.

Key Claims

• Trend trading is presented as a repeated-game system rather than a promise about the next trade: low hit rates can remain viable when losses are bounded and occasional winners are allowed to run.
• The proposed “纪律.md” has three layers: the eligible markets and instruments, explicit entry and exit states, and position rules at both instrument and portfolio level.
• A daily system can convert market data into signals, email or dashboard tasks, and target-position changes, while a fixed executor reduces interference from public commentary, profit excitement, and intraday emotion.
• The episode treats persistent decline as less damaging to trend systems than repeated high-level whipsaw, because a weak market may produce no entry while choppy markets generate false signals.
• Cross-asset comparison matters: the method scans equities, futures, bonds, and property rather than forcing trades in one depleted opportunity pool.
• Historical testing can build confidence, but repeated parameter changes to maximize Sharpe ratio or minimize drawdown can turn accidental history into overfit rules.
• Rule-based trend systems and factor models embody different approaches: the first accepts limited prediction and engineers asymmetric payoffs, while the second combines variables for predictive power and may produce smoother returns until model failure.
• The proposed attention-asset product would measure changes in topic, industry, account, and content attention for creator benchmarking, advertising, and topic selection rather than rank accounts only by followers.
• Personal attention is treated as a finite daily cost, while attention received from others can become distribution, revenue, or opportunity.

Key Quotes

“纪律与策略本质上是同一件事。”

“让利润奔跑。”

“自己的注意力是成本，别人的注意力是收入。”

Connections

• 面基 - show context linking investing, AI-enabled work, and personal agency.
• Programmable Trading Discipline / 可编程交易纪律, Trend Following, No-Prediction Trading, and Stop-Loss Discipline - rule, signal, repeated-play, and exit framework.
• Position Sizing, Kelly Criterion, Diversification Alpha, and Fat-Tail Risk - exposure and asymmetric-return logic.
• Quantitative Overfitting, Quantitative Investing, and Market Regime Shift - backtest, model, and changing-market qualifications.
• Attention as Asset / 注意力资产, Attention Capacity Selection, and Attention Industrialization - finite personal attention and monetizable public attention.
• One-Person Company, AI Investment Research, and Human Agency Under AI - AI-enabled solo production and human choice boundaries.

Contradictions

• No settled contradiction was found. The episode reinforces the prior 面基 distinction between signals and predictions while making execution more explicit through a written, programmable discipline.
• The reported 30%-40% win rate, 2-3 payoff ratio, market-stage labels, historical strategy performance, and product API capabilities remain guest-reported rather than independently audited.
• The attention-asset feature was still under development; its rankings, valuation estimates, advertising use, and predictive value remain proposals rather than validated results.
• The sponsored hair-loss segment is commercial health content and should not be treated as independent clinical evidence.