Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add skloxo/TideTrading --skill behavioral-financegit clone --depth 1 https://github.com/skloxo/TideTradingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/skloxo/tidetrading/behavioral-finance)<a href="https://agentmods.dev/skills/skloxo/tidetrading/behavioral-finance"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/behavioral-finance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/skloxo/tidetrading/behavioral-finance"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/behavioral-finance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.02377 |
| Opus 5 | $0.00021 | $0.01189 |
| Sonnet 5 | $0.00008 | $0.00475 |
| Haiku 4.5 | $0.00004 | $0.00238 |
Grade A, and why
behavioral-finance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to behavioral-finance — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Behavioral Finance Applications
Overview
Translate behavioral-finance theory into quantifiable trading signals and risk-control rules. Core assumption: market participants systematically deviate from rational decision-making, and these biases can be predicted and exploited.
Applicable scenarios:
- Behavioral interpretation and parameter optimization for momentum / reversal strategies
- Contrarian signals when market sentiment becomes extreme
- Debiasing mechanisms in portfolio construction
- Capturing behavior patterns specific to retail-driven China A-share markets
Core Concepts
Overreaction and Underreaction
Underreaction → momentum effect:
Mechanism: anchoring bias + conservatism
Investors anchor on old information and update insufficiently to new information
After an earnings beat, the stock price digests it gradually rather than all at once
China A-share evidence:
- Earnings-guidance beats still produce 3-5% excess return over the following 20 days
- After analyst rating upgrades, momentum often persists for 1-3 months
Quant signal:
SUE (standardized unexpected earnings) > 2σ -> buy and hold for 60 days
Top 10% 20-day return -> continue holding for 20 days (China A-share momentum cycles are shorter)
Overreaction → reversal effect:
Mechanism: representativeness heuristic + availability bias
Investors extrapolate recent trends too aggressively and ignore mean reversion
Panic / euphoria drives reactions beyond what fundamentals support
China A-share evidence:
- Rebounds after consecutive limit-downs (after 3 limit-downs, the average 20-day rebound is 8%)
- Big annual losers often earn 5-10% excess return the next year
Quant signal:
Bottom 10% of 250-day return -> buy and hold for 250 days
RSI(5) < 10 -> short-term rebound signal (5-10 days)
Key distinction:
| Dimension | Underreaction (Momentum) | Overreaction (Reversal) |
|---|---|---|
| Time scale | 1-12 months | <1 week or >12 months |
| Information type | Clear events (earnings / announcements) | Ambiguous information (sentiment / trend) |
| Best China A-share window | 20-60 days | 5-10 days (short term) / 1 year (long term) |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 209 lines · 42 tokens per session scan A 1ecc91cc4d5c
behavioral-finance is a skill published in the GitHub repository skloxo/TideTrading (10 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 2,377 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to behavioral-finance, differing in 0 lines, and is treated as a copy.
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