{
 "schema_version": 1,
 "generated": "2026-10-26",
 "id": "freqtrade-freqtrade--optimize-a-strategy-with-hyperopt-and-freqai",
 "repo": "freqtrade/freqtrade",
 "repo_url": "https://github.com/freqtrade/freqtrade",
 "repo_slug": "freqtrade-freqtrade",
 "title": "Tune a strategy with hyperopt and FreqAI",
 "page_title": "How to tune a strategy with hyperopt and FreqAI with Freqtrade",
 "summary": "Search strategy parameters with machine learning, or build a self-training model with FreqAI.",
 "who": "A quant hobbyist who wants to tune parameters or try adaptive machine learning models.",
 "problem": "Hand tuning parameters is slow and easy to overfit.",
 "outcome": "Optimised parameter sets ranked by a loss function, or a FreqAI model that retrains on the market.",
 "steps": [
  "Run `hyperopt` on your strategy and list results with `hyperopt-list`",
  "Inspect a result with `hyperopt-show` and see loss functions with `list-hyperoptloss`",
  "See available models with `list-freqaimodels` and read the FreqAI docs",
  "Check formulas with `recursive-analysis` and re-run `lookahead-analysis` before trusting results"
 ],
 "agent_prompt": "With freqtrade/freqtrade, run hyperopt on <strategy> over <period>, show the best three parameter sets, then backtest the best one on a later out of sample period and compare.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "2 hours",
 "basis": "readme",
 "caveat": "Optimised parameters often overfit the past, so always validate on unseen data and in dry-run. Not financial advice: paper trade first, and remember you can lose money.",
 "category": "Money",
 "tags": [
  "Money"
 ],
 "lang": "python",
 "requirements": [
  "Python 3.12 or newer, pip, git and TA-Lib, or Docker",
  "An accurate system clock synced to NTP",
  "About 2GB RAM, 1GB disk and 2 vCPU recommended",
  "Exchange API keys only when you move past dry-run"
 ],
 "risk": "sensitive",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/freqtrade-freqtrade/optimize-a-strategy-with-hyperopt-and-freqai/",
 "md": "https://dedupedai.com/use-cases/freqtrade-freqtrade/optimize-a-strategy-with-hyperopt-and-freqai/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/freqtrade-freqtrade--optimize-a-strategy-with-hyperopt-and-freqai.json",
 "api_version": "v1"
}