Do I need to know how to code to use Agenticks?

No. AlgoAgent accepts prompts and converts them directly into backtestable strategies. AlgoAgent does the same for TradingView indicators. No coding, no Pine Script, no programming background required.

AlgoAgent is the product. You describe a trading idea and it turns that into explicit entry, exit, sizing and risk rules, writes the strategy as sandboxed Python, runs it over the included market history, and hands back every trade so you can judge the idea yourself.

How long can I run a backtest?

As far back as the instrument's own history goes. US stocks and ETFs run from as early as 2000-01-01 through 2026-03-31 and supported futures from 2016, at 1m, 5m, 15m, 30m, 1h and 1d. There is no five-year cap. Results include an equity curve, a full trade log, drawdown analysis, regime breakdown, Monte Carlo resampled from the real trades, and a prop firm pass estimate.

What is the AlgoAgent live automation lane?

On the Agent plan an approved strategy places its own orders through the brokerage you connected with SnapTrade (Interactive Brokers, Alpaca, Wealthsimple, Webull, Moomoo, Coinbase, Kraken, Binance and more). It runs on Agenticks servers, not in your browser, so it keeps running when your computer is off. You approve the strategy, pick the account, set sizing and risk limits, and arm it explicitly. Editing it creates a new version that needs a new approval. Several supported brokers are read-only. Paper-test first.

Backtesting and Strategy Research

Backtesting, edge evaluation, expectancy, drawdown, and strategy research explained for traders who want evidence before risk.

Guides in this category

Backtesting Trading Strategies: What Retail Traders Usually Miss: The common backtesting mistakes that make results look better than reality: sample size, overfitting, regime dependency, and why one good backtest is not enough.
Win Rate vs Risk Reward: Which Matters More?: A data-driven look at how win rate and risk-reward ratio interact to determine overall strategy expectancy and profitability.
What Is Expectancy in Trading?: How expectancy measures average profit or loss per trade and why it is a more complete metric than win rate alone.
How to Know If a Trading Strategy Has an Edge: The criteria and tests that help traders determine whether a strategy has statistical edge or is performing within the range of random chance.
Why One Good Backtest Does Not Prove a Strategy Works: Why a single strong backtest result is not sufficient evidence of edge, and what additional validation is needed.
The Trader's Guide to Drawdown: What drawdown measures, how to interpret max drawdown and drawdown duration, and what those numbers mean for real trading conditions.
How to Read Backtest Output Without Fooling Yourself: A structured approach to evaluating backtest output without falling for common biases, including cherry-picked start dates and curve-fitting.
Common Backtesting Mistakes That Destroy Strategy Confidence: The most frequent errors in backtest design that make results unreliable and how to avoid them in a structured evaluation process.
Why Sample Size Matters in Trading Strategy Testing: How to think about trade sample size when evaluating a strategy's statistical validity and why small samples produce unreliable conclusions.
How to Compare Two Trading Strategies Properly: A structured method for comparing strategy performance across the same time period, market regime, and risk-adjusted return metrics.
AI Backtesting Explained: From Prompt-driven Idea to Tested Strategy: AI backtesting turns a written strategy idea into testable rules and runs it against historical data. What the workflow does, what it measures, and where the results still need a careful human read.
How to Backtest a Trading Strategy With AI and No Code: You describe the rules in plain language, the AI converts them into a structured strategy, and you review the historical performance. A walkthrough of the no-code backtesting workflow for retail traders.
Why Backtested Strategies Fail in Live Trading: A strategy that looked clean in backtest can fall apart live because of slippage, regime change, overfitting, and survivorship in the test window. The structural reasons results do not carry over, and how to test for them.
When a Backtest Looks Great but Loses Money Live: The most common reason traders lose confidence in their process is a beautiful equity curve that does not repeat. A practical checklist for diagnosing the gap between historical results and live performance.
How to Turn a Strategy Idea Into a Backtest Using AI: From a one-paragraph idea to a structured, testable rule set. How AI backtesting tools convert intent into entries, exits, and risk logic you can actually evaluate before risking capital.
How to Backtest a Strategy From a Research Paper or PDF: Academic and vendor PDFs describe edges in prose, not code. How traders extract the rules from a research paper and turn them into a backtest instead of taking the claim on faith.
Overfitting: Why a Winning Strategy Suddenly Stops Working: Overfitting makes a strategy look excellent on the data it was built on and average everywhere else. How to recognize a curve-fit result and how to test whether an edge is real or coincidental.
TradingView Alternatives for Backtesting a Strategy: TradingView's strategy tester is convenient but limited for serious research. A look at what traders move to when they need deeper backtesting, walk-forward analysis, and Monte Carlo, and how AI backtesting fits in.
Best Backtesting Software for Retail Traders, Compared: The backtesting tools retail traders actually use, from code-heavy platforms to no-code AI backtesting. How they differ on data, realism, and how much coding each one expects from you.
A No-Code Alternative to QuantConnect for Backtesting: QuantConnect is powerful but expects Python. How traders who do not want to code run structured backtests by describing the strategy instead.
The Limits of the TradingView Strategy Tester: The built-in strategy tester is great for a first look and weak for robustness. The specific limitations around data, repainting, and sample size, and what to check before trusting a result.
AI Backtesting vs Traditional Backtesting: Traditional backtesting expects you to code the rules, AI backtesting converts plain language into testable logic. How the two compare on speed, control, and where errors creep in.
Walk-Forward Analysis, Explained: Walk-forward testing checks whether a strategy holds up on data it was not tuned on. How it works and why it is a stronger robustness test than a single in-sample backtest.
Monte Carlo Simulation in Backtesting, Explained: A single backtest is one path through history. Monte Carlo reshuffles the trades thousands of times to show the realistic range of drawdowns and outcomes.
What Counts as a Good Sharpe Ratio in a Backtest: Sharpe ratio measures return against volatility, but the number is easy to misread. How to interpret it in a backtest and why it should never be judged alone.
Max Drawdown, Explained for Strategy Testing: Max drawdown is the largest peak to trough drop a strategy took. Why it often matters more than total return when judging whether you could actually trade a system.
Expectancy vs Win Rate: Why a High Win Rate Can Lose: A strategy can win often and still lose money if the losers are large. How expectancy combines win rate and average win and loss into a more honest measure.
Backtesting From a Prompt-driven Strategy Description: You write the rules in normal language and the AI converts them into a structured, testable strategy. How prompt-driven backtesting works and where wording matters most.
AI Backtesting Across Crypto, Equities, and Futures: The same idea can behave very differently by asset class. How AI backtesting handles crypto, equities, and futures, and what changes in the data and assumptions.
How to Read Backtest Results Without Fooling Yourself: An equity curve is the easiest part to misread. How to weigh drawdown, trade count, and regime behavior so a backtest informs a decision instead of selling you on one.
How Many Trades Does a Backtest Need to Be Meaningful: A great result on twelve trades is noise. How sample size affects confidence and why trade count is one of the first things to check in any backtest.
Adding Slippage and Commissions to a Realistic Backtest: Frictionless backtests flatter a strategy. How slippage, commissions, and fills change the picture and why a realistic test matters before automation.
Profit Factor Explained: Do Your Winners Beat Your Losers?: Profit factor is the dollars your winners made divided by the dollars your losers cost. Here is what it means, how to calculate it, and why a great win rate can still lose money.
In-Sample vs Out-of-Sample: The Backtest Split That Stops Overfitting: In-sample data is what you build a strategy on; out-of-sample is the untouched slice you test it on. Here's how that one split catches overfitting before it fools you.
How to Backtest a Trading Strategy (Step by Step): Backtesting means running your trading rules against past price data to see how they would have performed. Here is the plain path from a rough idea to a tested result you can actually read and trust.
How to Backtest a Strategy Without Coding: You do not need Python or Pine Script to test a trading idea. This guide shows how to write your rules, hand them to an AI agent, and read the results like a beginner.
How to Turn a Research Paper or PDF Into a Backtest: Have a strategy PDF or research paper full of trading rules? Here is how to pull those rules out and test them on past market data, so you see how the idea actually held up before risking a cent.

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