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.

Algorithmic Trading and Strategy Building

How to build, test, and structure trading strategies with AlgoAgent or manual rules, without building infrastructure from scratch.

Guides in this category

What Is Algorithmic Trading and How Does It Work?: Algorithmic trading explained: how rule-based strategies work, how traders use backtesting to evaluate ideas, and what realistic automation looks like for retail traders.
Algorithmic Trading for Retail Traders: What Is Realistic?: An honest look at what retail traders can actually achieve with algorithmic trading and what requires more infrastructure than most assume.
How to Build an Algo Trading Strategy Without Starting From Code: Tools and methods that let retail traders structure and test algo trading strategies without writing code from scratch.
Backtesting Explained: How Traders Test Ideas Before Risking Capital: A clear explanation of how backtesting works, what it measures, and where it can mislead traders who read results uncritically.
The Difference Between Backtesting, Paper Trading, and Live Trading: How each stage of strategy validation differs in what it tests, what it proves, and what it cannot tell you.
Why Most Algo Trading Strategies Fail in Real Markets: The common reasons rule-based strategies underperform in live conditions versus their backtested results.
How to Turn a Trading Idea Into a Rule-Based Strategy: A practical framework for converting a trading concept into a defined set of rules that can be tested and evaluated.
Algorithmic Trading vs Manual Trading: Which Fits Your Style?: A comparison of how each approach handles decision-making, consistency, and scalability across different trader profiles.
A Practical Framework for Testing Trading Strategies: How to structure a rigorous strategy evaluation process that goes beyond headline win rates and backtested returns.
The Biggest Mistakes Retail Traders Make When Automating Strategies: Common errors in strategy design, testing, and deployment that cause automation projects to fail.
What an AI Trading Agent Actually Does: An AI trading agent researches an idea, builds the rules, runs a backtest, and can prepare it for live execution. Here is what that workflow looks like in practice and where a human still has to make the call.
How to Automate a Trading Strategy Without Writing Code: Automating a strategy used to mean building broker infrastructure from scratch. AI agents and connected execution lanes now let retail traders test and run rule-based strategies without writing the plumbing themselves.
How an AI Agent Can Backtest a Strategy and Then Run It Live: The gap between a passing backtest and live execution is where most automation projects break. This is how an AI agent moves a strategy from historical testing to a live lane through your own connected brokerage.
Algo Trading for NQ Futures: How It Actually Works: NQ moves fast, so a rule-based approach has to define entries, exits, and risk precisely. A practical look at how algorithmic trading is applied to Nasdaq futures without overfitting the logic to one good week.
How to Automate a Futures Strategy Through Your Own Account: Running a tested futures strategy automatically does not require a custom trading server. Here is how traders connect a validated strategy to a futures account and keep execution under their own control.
What Automated Trading Realistically Looks Like for Retail Traders: A grounded breakdown of what retail automated trading can and cannot do, why full hands-off systems are rarer than the marketing suggests, and where structured tools genuinely save time.
Order Types for Futures Traders: Market, Limit, Stop, Stop-Limit: Each order type controls a different tradeoff between certainty of fill and certainty of price. A plain breakdown of market, limit, stop, and stop-limit orders for futures execution.
Limit vs Market Orders: How Execution Differs in Fast Markets: In fast NQ conditions the choice between a market and a limit order changes slippage and fill risk. How execution differs and why automated strategies have to choose deliberately.
Stop Orders, Brackets, and OCO Orders for Futures: Bracket and OCO orders attach exits to an entry so risk is defined at fill. How these order structures work and why automated futures strategies rely on them.
Automating a Futures Strategy Without NinjaTrader Coding: NinjaScript gives full control but expects C#. How traders connect a tested futures strategy to live execution without writing platform-specific automation code.
Can AI Write a Trading Strategy? What It Can and Cannot Do: AI can turn a described idea into structured, testable rules quickly. Where it genuinely helps, and where a human still has to define edge, risk, and judgment.
Using ChatGPT for Trading Strategies: The Real Limits: General chat models can draft logic but cannot backtest or validate it. Why purpose-built AI backtesting matters when you want to know if an idea actually held up.
The Opening Range Breakout, Applied to NQ Futures: The opening range breakout is a defined, testable framework rather than a guaranteed setup. How NQ traders structure the rules and stress test them across different sessions.
AI Trading Agent vs a Traditional Trading Bot: A classic bot runs fixed code, an AI agent researches, builds, tests, and adapts the workflow with you. How the two differ and where each fits a retail trader's process.
What an AI Trading Agent Can and Cannot Do for You: An AI agent can research, build rules, backtest, and prepare execution, but it does not remove risk or guarantee outcomes. A grounded look at the boundary between automation and judgment.
An AI Trading Agent for Futures Traders: Futures move fast and reward defined rules. How a futures-focused AI agent helps build, test, and prepare NQ and ES strategies without writing the plumbing yourself.
How to Connect a Tested Strategy to Your Broker for Automation: Automating execution means linking a validated strategy to your own connected brokerage, not handing money to a third party. How the connection works and what stays under your control.
Closing the Gap Between a Backtest and Live Execution: Most automation projects break between a passing backtest and a real fill. The practical steps that move a validated strategy into a live lane through your own brokerage.
Server-Side vs Browser-Based Strategy Automation: Where a strategy actually runs changes reliability. How server-side execution differs from browser-based automation and why an always-on engine matters for live rules.
Why to Paper Trade Before Automating Live: Paper execution validates the wiring before real capital is involved. How traders use a paper lane to confirm an automated strategy behaves as tested.
Can AI Actually Trade For Me? What an AI Trading Agent Really Does: Yes, AI can place trades for you now, but only on rules you give it, and it can't promise profit. Here's what today's trading agents really do, and how to try one safely.
Can You Automate Trading on Wealthsimple? What Is Actually Possible in Canada: Wealthsimple can automate your investing with recurring buys, but it will not run a trading strategy for you. Here is what its automation actually does, and how Canadians can run tested rules through a connected broker instead.
How to Turn a Trading Idea Into a Testable Strategy: A trading idea like "buy when price dips" isn't testable yet. To turn it into a strategy, you write exact rules for when to enter, when to exit, and how much to risk. Here is how.
How to Automate a Trading Strategy: Automating a trading strategy means software places your buy and sell orders for you, following rules you set. Here is what that really looks like for a retail trader, step by step, starting with paper money.
How to Automate Trading Through Your Own Broker: Automating trades on your own broker means you connect your existing brokerage account, approve one strategy you understand, and let it place orders under rules you set. Here is how to start safely, paper first.

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