AskFutures
Overview
AskFutures allows traders and investors to describe a futures trading strategy in plain English, which is then interpreted by an AI chatbot to automatically backtest against historical market data. The tool aims to make strategy testing accessible without coding skills, enabling rapid iteration and validation of trading ideas. It is particularly useful for those exploring futures markets and wanting to quickly evaluate the performance of simple or complex strategies through natural language interaction.
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Key Features
- Natural language strategy input
- Automated backtesting on futures data
- AI-powered interpretation of plain English descriptions
- Quick iteration on strategy variations
- No coding required
Pros
- Eliminates need for programming knowledge
- Speeds up strategy testing cycle
- Accessible to non-technical users
- Focuses specifically on futures markets
Cons
- Limited to futures strategies only
- Accuracy depends on AI understanding of complex strategies
- Historical backtest results may not predict future performance
Who Is This Tool Best For?
Traders and investors looking to backtest futures strategies without programming
Frequently asked questions about AskFutures
What does AskFutures actually do?
It runs historical simulations of futures trading ideas that you describe in ordinary language rather than in code. You state a rule — enter when a moving average crosses, exit after a fixed number of days, only trade in a given session — and the tool interprets it, applies it to past price data and reports how that rule would have performed. The unusual part is the interface, not the underlying method: backtesting itself is decades old and well understood.
Do I need to know how to code?
No, and that is the point. Comparable work in a programming environment means writing the strategy, loading the data and handling the accounting yourself — a real barrier if you are not a developer. Describing the rule in plain English removes that step. What it does not remove is the need to specify the rule precisely: an ambiguous description produces a simulation that tests something other than what you had in mind, and the output will not tell you that.
Does a good backtest mean the strategy will work?
No, and this is the single most important limitation. A backtest shows how a rule would have behaved on data that already exists. It cannot account for the market conditions that differ from that history, for the fact that many rules were tried before this one was chosen, or for costs and slippage that are modelled more optimistically than reality delivers. A favourable result is a reason to look more carefully, never evidence of future profit.
What about overfitting and curve fitting?
It is the main trap, and a conversational interface makes it easier to fall into because iterating is so quick. Adjust the parameters often enough against the same history and you will eventually find a rule that fits the past beautifully and predicts nothing. The defence is the same as in any quantitative work: keep a portion of the data untouched while you develop, test the rule there once, and treat a strategy that needs constant re-tuning as a failure rather than a work in progress.
How is AskFutures priced?
It does not publish a simple fixed price, and it is therefore listed on ToolFlect with pricing to be checked with the provider. Tools in this category commonly charge by subscription or by usage, with the cost tied to how much historical data and compute a simulation consumes. Because the pricing model affects how freely you can experiment — and experimenting freely is the whole reason to use it — the commercial terms matter almost as much as the features. Confirm them directly before you build a workflow around it.
Who should avoid this kind of tool?
Anyone looking for recommendations rather than a testing environment. AskFutures evaluates rules you supply; it does not tell you what to trade, and it has no view on whether a market is worth trading at all. If you want signals or managed exposure, this is the wrong category of product. It is also unsuitable as a substitute for understanding the instrument you are testing — futures carry leverage, and a simulation cannot convey what a drawdown feels like in a live account.
Use cases for AskFutures
Checking an idea before committing capital
The most defensible use. Before risking money on a hunch, describe the rule and see how it behaved historically, including the worst stretches rather than only the average outcome. Look at drawdown and the number of trades as closely as the return: a rule that made money over nine trades has told you almost nothing, however good the final figure looks.
Screening several variations of one strategy
Describing rules in language makes it practical to compare a handful of closely related versions — different exit rules, different session filters, a stop versus a time-based exit. Used carefully this is genuine research. Used carelessly it is the fastest route to overfitting, which is why the comparison should be decided in advance and the results kept somewhere you can see how many attempts you actually made.
Learning how systematic trading is evaluated
For someone moving from discretionary trading, this is a low-friction way to meet the standard evaluation vocabulary — expectancy, drawdown, exposure, trade count, parameter sensitivity. Running a few rules and reading the output honestly teaches more about why most ideas fail than any amount of theory, provided the learner resists treating a good simulation as a plan.
Pricing
AskFutures is catalogued on ToolFlect with a score of 4.3 and pricing shown as requiring a check with the provider, because it does not publish a straightforward fixed plan. Expect the commercial model to combine a subscription with some limit on simulation volume or historical data access. For a researcher the important question is whether the allowance lets you run a realistic number of variations, since a tool that rations experimentation undercuts its own purpose. Verify the current terms on the official AskFutures site before relying on it.
Alternatives to AskFutures
The alternatives split between no-code platforms and code libraries, and the right one depends on whether you want convenience or control:
- QuantConnect — a full algorithmic trading platform with real brokerage integration and far deeper data, at the cost of writing code.
- Backtrader — a free, widely used Python backtesting framework. Maximum control and no subscription, if you are comfortable programming.
- TradingView — its Pine Script strategy tester covers a large share of common ideas inside an environment you may already use for charting.
- Composer — no-code strategy building aimed more at portfolio-level allocation than at discretionary futures rules.
Each is listed on ToolFlect for comparison.
How to get started with AskFutures
Write the rule down before you describe it
Put the entry, exit and position size in plain sentences first, and check that a colleague could follow them without asking a question. Vague rules are the most common reason a simulation is misleading, because the tool will resolve the ambiguity somehow and you will not be told which way. Precision before automation.
Hold back part of the history
Decide in advance which years you will develop against and which you will leave alone, then keep to it. Testing on the reserved period once at the end is the only inexpensive protection against overfitting available to you, and its value disappears entirely if you peek at it repeatedly while tuning.
Read the drawdown before the return
When the results arrive, look at the worst peak-to-trough decline, the number of trades and the sensitivity of the outcome to small parameter changes before you look at the headline profit. A rule you would not have been able to hold through its worst stretch is not a rule you can trade, regardless of what the total return says.
Treat the output as a hypothesis, not a forecast
Nothing produced here predicts live results. Historical simulation cannot price in the execution you will actually get, the conditions that differ from the sample, or the effect of your own decisions under pressure. Move forward, if at all, with the smallest live size that still feels real, and expect the live behaviour to differ from the simulated one. That expectation is the correct starting assumption, not pessimism.