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.

Agenticks Platform Education

How Agenticks's tools connect: AI strategy building, backtesting, PineScript, stock research, market terminal data, and the AlgoAgent live automation lane.

Guides in this category

What Is Agenticks?: Agenticks is a trading research platform for retail traders, prop firm traders, quant-style builders, and self-directed investors who want advanced tools without disconnected setups.
How Agenticks Helps Traders Build More Structured Workflows: How Agenticks connects research, strategy building, backtesting, indicators, market context, and operational handoff into one structured trading workflow.
AlgoAgent Explained: From Idea to Testable Trading Logic: A walkthrough of how AlgoAgent converts a trading idea into a structured rule set that can be tested in Backtest Review.
Terminal Explained: Building Better Market Awareness: How Agenticks's Terminal organizes live market data, sector performance, global context, and GEX into a structured daily workflow.
Agenticks Indicators: What They Are Built For: The design philosophy behind Agenticks's proprietary indicators, including what they measure, what they do not claim, and how they fit into a structured workflow.
How Agenticks Supports TradingView and PineScript Workflows: How AlgoAgent, the Indicator Library, and AlgoAgent connect to serve traders who use TradingView as their primary charting platform.
Why Agenticks Focuses on Tools, Not Trade Promises: The philosophy behind Agenticks's product design: structured research tools, not signal services, guaranteed outcomes, or managed trading.
Building a Full Trading Workflow Inside Agenticks: How to use Agenticks's tools in sequence from market research through strategy building, backtesting, indicator generation, and operational handoff.
Agenticks for Retail Traders, Prop Traders, and Quant Builders: How different trader profiles, from discretionary retail traders to prop firm challengers and quant-style builders, use Agenticks's tools differently.
AlgoAgent: AI Backtesting From Prompt-driven Ideas: AlgoAgent converts a written strategy idea, research, or PDF into a structured backtest you can review in detailed backtest results. What it does, how the workflow runs, and what to check in the results.
AI Trading Tools That Actually Help, Not Predict: The useful role of AI in trading is research, testing, and code generation, not predicting the next candle. How Agenticks's AI tools fit into a structured workflow without overpromising.
AI Trading Tools That Are Genuinely Useful in 2026: The honest role of AI in trading is research, code generation, and testing, not predicting the next candle. How to tell a useful AI trading tool from a black box that overpromises.
Building and Testing a Strategy by Chatting With an AI Agent: Describe the idea in plain language and an AI agent turns it into testable rules, runs the backtest, and shows the results inline. What that conversational workflow looks like end to end.
From Research to Live: How an AI Trading Agent Workflow Runs: Research the idea, build the rules, backtest, review, then optionally connect to a live lane. How an AI agent moves a strategy through each stage with a human checkpoint.
Running a Custom Research Study With an AI Agent: Beyond preset strategies, an AI agent can design a custom measurement to test an idea against your own data. How in-chat research studies turn a question into evidence.
Multi-Broker Automation, Explained: One automation lane can route to different brokers through a secure connection layer. How multi-broker execution works and why it keeps orders tied to your own accounts.
Automating Trades on Wealthsimple, Webull and Robinhood: Retail brokers were not built for custom automation, but secure connection layers now bridge the gap. How a tested strategy can route orders through your own connected retail account.
Automated Trading Through Your Own Account, Not a Black Box: Letting a strategy run does not mean giving up control or copying someone else. How execution stays in your own connected account with rules you defined and tested.

Agenticks

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