Results for “backtesting”

Reviews trading strategy drafts for edge plausibility, overfitting risk, sample size adequacy, and execution realism, outputting PASS/REVISE/REJECT verdicts with confidence scores.

PTES-aligned adversarial security audit for backend, frontend, and mobile applications. Produces a CVSS-scored Hacker Report with verified PoCs and phased remediation.

Escribe tests de frontend centrados en el usuario con Vitest, Testing Library y Playwright, cubriendo unit, integración y E2E, y configura el runner de tests.

Verifies that tests for already-implemented behavior are meaningful by temporarily breaking the production code and confirming the test fails for the expected reason.

Prevents AI-introduced regressions with sandbox-mode API testing, automated bug-check workflows, and patterns that catch blind spots where the same model writes and reviews code.

Implements comprehensive backtesting capabilities for Pine Script indicators and strategies. Use when adding performance metrics, trade analysis, equity curves, win rates, drawdown tracking, or statistical validation. Triggers on "backtest", "performance", "metrics", "win rate", "drawdown", or testing requests.

World-class backend engineering - distributed systems, database architecture, API design, and the battle scars from scaling systems that handle millions of requestsUse when "backend, api, database, postgres, mysql, mongodb, redis, graphql, rest, authentication, authorization, caching, queue, background job, webhook, migration, transaction, n+1, rate limit, server, node.js, python, go, backend, api, database, architecture, performance, reliability, security" mentioned.

Provides structured reverse engineering techniques for analyzing compiled, obfuscated, packed, or virtualized targets including binaries, APKs, WASM, firmware, and custom VMs using static and dynamic analysis workflows.

Provides a structured methodology for authorized Android and iOS reverse engineering, covering static analysis, dynamic instrumentation with Frida and Objection, SSL pinning bypass, root/jailbreak detection evasion, and cryptographic key extraction.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Guides backend development across API design, authentication, security, performance, architecture, and testing, with decision guides and defaults for production-ready systems.

Detect when backtest iteration has stalled and generate structurally different strategy pivot proposals to break out of local optima.

Diagnose failures from runtime evidence before editing code.

Turn assumptions, funnel opportunities, and product questions into a prioritised, feasibility-checked experiment backlog. Filters by traffic reality, metric latency, and method feasibility — not just ICE/RICE scoring. Maintains a living portfolio with status (idea → designed → running → readout → archived). Load when the user says "what should we test next", "build an experiment backlog", "prioritise our tests", "where should we experiment", "what's worth testing", or when the experimentation orchestrator routes here.

Designs and backtests systematic trading strategies using technical indicators, fundamental analysis, and statistical models, with clear entry/exit rules, risk controls, and documentation.

Test Fleet

Diagnose and eliminate performance bottlenecks across web, API, database, and code with load testing, profiling, and optimization techniques.

Strategic decision retrospective — review research decisions against actual outcomes, update confidence levels

Weekly engineering retrospective. (gstack)

`analysis-agent`/`task-agent`/`review-agent`: use for recurrence guards on known defects, incidents, or escaped failures; skip speculative risk without a prior failure mechanism.

Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating. Use when the user runs /ar:resume or asks to pick up a previously started autoresearch experiment.

Run a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).

Guides backend development across API design, authentication, databases, performance, security, scalability, and testing, with quick decision guides and defaults for production-ready systems.

Rehearse

Retro

Design and evaluate A/B tests with power, sample size, and robust metric interpretation. Use when: (1) planning controlled experiments, (2) reading p-values/effects, (3) sequential testing safeguards. NOT for: dark-pattern optimization.

Analyzes repeated agent failures or noisy routing and proposes evidence-backed changes to instructions, skills, gates, or runbooks.

`analysis-agent`/`task-agent`/`review-agent`: use for component, route, browser, accessibility, and API-backed UI-state tests; skip backend, portfolio, and release-verdict work.

Validate ransomware recovery plans by testing backup restore operations, measuring RTO/RPO targets, verifying data integrity, and documenting recovery gaps in an isolated lab environment.

Runs a side-by-side walk-forward backtest of a Bull Put Spread strategy with and without a FearScore entry signal, comparing performance metrics over ~8 years of daily data.