AI Backtesting Trading Strategy (Renko Charts, Bitcoin Perpetual Futures)

AI-backtested Renko chart strategy for Bitcoin perpetual futures, evaluated over 2.7 years of data and decoded into Pine Script for TradingView.

Published · Updated · Methodology: Mixed

Part of: Renko Charts

  • Methodology: Mixed
  • Content type: educational
  • Timeframes: 2.7 years of data
  • Markets: Bitcoin Perpetual Futures

Indicators used

  • Renko Charts

Source video

Decoded from: How To Use AI To Backtest Your Trading Strategy (My Strategy Turned 10k into 700k in 2.7 years!) by The Good, The Bad And The Bitcoin — watch the original

Key timestamps:

  • 0:00 - Introduction & Why Backtesting Matters
  • 0:39 - The Problem with Manual Backtesting
  • 1:31 - Setting Up Claude Code
  • 2:15 - Exporting TradingView Data
  • 3:51 - Creating Development Guidelines (Claude.md)
  • 4:58 - Starting the Analysis with Claude
  • 6:40 - Understanding the Data Structure
  • 10:04 - Setting Backtesting Parameters
  • 17:03 - Analyzing Results & Performance Metrics
  • 22:22 - Next Steps: Building a Trading Bot

Strategy overview

Renko charts strip time out of the picture and print a new brick only when price travels a fixed distance, so the chart advances on movement rather than on the clock. That is where the similarity with most Renko entries ends: this one comes from a video whose real subject is not the setup but the *testing pipeline* around it. The Good, The Bad And The Bitcoin uses a Bitcoin perpetual futures idea drawn on Renko bricks as the specimen, and spends the video wiring up an AI coding assistant to evaluate it.

The chapter list tells the story — the problem with manual backtesting, setting up Claude Code, exporting TradingView data, writing a development-guidelines file, then handing the analysis over to the model. The strategy is the input; the deliverable is the workflow. Renko is not incidental to that workflow either, because a brick series is a derived representation rather than raw candles: what gets exported to the AI depends on how the bricks were constructed in the first place, and the test window used here spans roughly 2.7 years of data.

Worth reading with that in mind: the headline figure in the video's title is the creator's own self-reported result from a self-built backtest, not an independently verified track record, and Renko in particular is unforgiving here — brick-based series can make fills and exits look cleaner in simulation than they are in live execution. This page leans on the concept and on what the source video demonstrates; treat it as a look at an AI-assisted backtesting method applied to Renko, rather than a rule-by-rule system to copy.

Topics

ai backtesting · trading strategy · bitcoin perpetual futures · renko charts strategy · btc trading strategy · pine script strategy · tradingview strategy · algorithmic trading · mixed methodology · crypto trading strategy · ai trading strategy · bitcoin strategy

Frequently asked questions

What is a Renko chart?

A Renko chart plots price as a series of fixed-size bricks, adding a new brick only when price moves far enough in one direction. Time is not on the axis, so quiet periods compress and trends stretch out, which is why Renko is often used to smooth noise out of a chart.

What does this video actually cover?

It walks through an AI-assisted backtesting workflow: exporting price data from TradingView, setting up an AI coding assistant with a development-guidelines file, and having it run the analysis on a Bitcoin perpetual futures idea built on Renko charts, using roughly 2.7 years of data.

Should I trust the return figure quoted in the video title?

Treat it as an unverified, self-reported backtest result from the creator's own code, not a live audited track record. Single-run backtests are especially fragile on Renko because brick construction and fill assumptions can flatter the simulated exits; re-testing the logic yourself under your own assumptions is the only way to know.

Can an AI assistant reliably backtest a trading strategy?

It can write and run the code far faster than doing it by hand, which is the case the video makes — but the model inherits whatever assumptions you give it about data quality, fees, slippage and how bricks are formed. The result is only as sound as the spec behind it. Strategy Decoder extracts the structure of strategies like this one from video sources so you can evaluate and test them yourself.

About this strategy page

This trading strategy was decoded by Strategy Decoder's AI from a public YouTube trading video and turned into a structured, reviewable specification. In the interactive app this page shows the full entry and exit logic, risk management settings, the indicators involved with their parameters, AlgoWizard-compatible logic and a Pine Script export ready for TradingView backtesting — plus an automated backtest verdict when one has been computed for this strategy.

Strategy Decoder catalogs 2,229 decoded strategies. Each one is extracted with confidence scoring, cross-linked to the indicators it uses, and kept up to date as new videos are processed daily. Load this page with JavaScript enabled to use the interactive tools, or start from the strategy explorer to filter by methodology, market and timeframe.

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