Build and Backtest a Trading Strategy From a Market Thesis

Example: turn a market idea into rules you can test

An illustrative prompt is: “I think the broad US stock market may regain momentum when SPY rises above its 50-day moving average. Show me an entry rule, a condition for leaving, and what would invalidate the idea.” This is an example of a testable question, not a current market recommendation or an actual CommonQuant backtest result.

Inspect the generated instrument and moving-average period, the exact entry and exit conditions, the historical test window, drawdown, and the news or market condition that would invalidate the thesis. Change a rule you disagree with and test again. If you activate the reviewed strategy, CommonQuant monitors conditions and sends signals; you decide whether to place any order with your own broker.

1. Begin with a market thesis

A thesis explains why an observable market relationship might matter. “Weak jobs data may keep the Fed on hold, which could support Bitcoin” contains a catalyst, an expected direction, and an asset relationship. “Tell me what to buy” does not. The pipeline extracts the structured thesis — theme, sectors, direction, horizon, risk appetite — along with explicit invalidation conditions, so the resulting strategy knows what it believes and what would prove it wrong.

You can bring your own thesis or follow a public CommonQuant idea. The breaking-news engine also scores significant financial headlines and turns high-signal stories into ready-to-follow strategies that cite the source article. Following an idea does not turn the source story into a prediction or financial advice.

2. Ground the thesis in real fundamentals data

Ticker and universe selection is grounded in data, not model memory. CommonQuant selects from several thousand US equities and ETFs built from SEC EDGAR company data and listings across Nasdaq, NYSE, NYSE American, and Cboe, with sector percentiles computed from XBRL fundamentals. It also supports major crypto assets such as Bitcoin, with intraday coverage that varies by asset class. The platform does not support FX or futures.

Every proposed ticker is checked against the supported security universe. Unknown symbols are removed instead of being accepted because an AI model produced them. That deterministic validation is important: a readable strategy is only useful if its instruments actually exist in the system that will monitor it.

3. Compile the research into a full strategy language

The research compiles into CommonQuant’s strategy DSL — a real strategy language, not a fill-in template. It expresses 20+ indicators across momentum, trend, volatility, volume, and channel or level tools (RSI, MACD, EMA, SMA, ADX, Stochastic, ATR, Bollinger Bands, VWAP, OBV, SuperTrend, Donchian Channels, Pivot Points, Ichimoku Cloud, and more), plus chart and price-action patterns, divergences, Fibonacci and support/resistance structure, position and portfolio state, cross-asset triggers, and risk-based sizing. A real compiler validates every strategy, and an automatic backtest over a timeframe-dependent historical window with walk-forward checks — plus a stress test on the backtest evidence — gates it before it can run.

Before a strategy ships, it passes adversarial AI review: a risk auditor, a red team, a regime skeptic, and a diversification critic each challenge the draft from their own angle. An entry rule should say what confirms the thesis; an exit rule should say when the setup has failed, completed, or changed. CommonQuant can generate multiple variants for one thesis because there is often more than one reasonable translation of an idea into rules — variants are alternatives to inspect, not guaranteed outcomes.

CommonQuant also offers an agentic strategy mode. The distinction matters: rule-based strategies evaluate deterministic, auditable conditions, while agentic strategies are AI-managed. This page focuses on the compiled, inspectable rule-based path.

4. Allocate with institutional quantitative methods

Portfolio weights are not guessed by a language model. Allocations are computed by real quantitative methods — Black-Litterman, hierarchical risk parity, risk parity, max-Sharpe, and min-variance — run deterministically in-engine and selected by risk-adjusted performance. The AI explains the allocation; it never invents the numbers.

Before going live, check the selected instruments, timeframe, indicator parameters, allocation, and both sides of the rule set, then use the dedicated backtest view to test the strategy against historical data yourself. Historical behavior is not a forecast: pay attention to the number of observations, changing volatility, transaction costs outside the platform, and whether the original market regime still applies.

5. Monitor live conditions — and the world

Compiled strategies monitor supported market data continuously on timeframes from 1 minute through 1 week. When an entry or exit condition becomes true, the strategy emits a signal you can audit down to the rule. The in-app inbox is available on every plan; email, Telegram, SMS, and WhatsApp are paid-plan channels, with SMS and WhatsApp allowances varying by tier.

Because every strategy carries explicit invalidation conditions from its thesis, an AI news judge runs hourly against live campaigns — and when fresh headlines confirm those conditions, it can conclude and settle the campaign, notifying the owner and its followers. A signal is the end of the monitoring workflow, not the beginning of automated execution: CommonQuant does not connect to a broker, and you act — or decline to act — through your own brokerage account.

What “no-code” means here

No-code means you do not have to write the strategy DSL yourself. It does not mean the economic choices disappear. You still decide what you believe, whether the selected tickers are suitable, how much risk is appropriate, and what evidence would change your view. The pipeline does the research and translation work between a thesis and an auditable ruleset; it does not replace diligence.

CommonQuant is free to start with no credit card. Starter, Pro and Max offer higher weekly usage limits and more reporting and alert capacity. CommonQuant is a quantitative tool for alpha validation, not financial advice, and it does not guarantee returns.

How to judge a backtest before you activate a strategy

Start with the exact entry, exit, sizing, and invalidation rules. Check when the signal becomes knowable: a rule triggered by a closing price cannot assume an earlier fill at that same close. Look at the test dates, number of triggered trades, the worst drawdown, and results outside the period used to tune parameters. A strategy with no triggered trades has no trade-level performance evidence; a single winning period is also too little to establish a durable edge.

Stress the result against costs. For illustration only, buying at $100 and selling at $101 is a 1% gross gain per share. If fees, spread, and slippage together cost $0.30 per share for the round trip, the gain falls to $0.70, or 0.7% of the $100 entry, before taxes. Those are example numbers, not a CommonQuant result or a prediction. Use costs and fills relevant to your broker and market rather than assuming the simulated price is executable.

If a later signal or paper trade differs from the backtest, compare the exact rules, symbol, timeframe, and test dates first. Check when each rule became knowable: a daily closing-price condition cannot be confirmed before that session closes. Then separate the monitored signal from a simulated paper fill and from any order you placed with your broker. Compare your actual fees, spread, slippage, missed orders, and execution delay; CommonQuant cannot verify broker fills because it does not execute trades. Finally, check whether the market regime changed or the strategy was tuned too closely to one period.

Finally, compare the hypothetical result with forward paper-trading observations and the public track record. Check the number of settled campaigns and closed paper trades before interpreting any win rate. CommonQuant’s ledger is self-published paper trading, not broker-executed account performance or an independent audit. A paper trade can test whether rules produce signals over time, but it cannot establish what a broker would have filled.

• What exact rule caused each entry and exit?
• Was each signal calculated only from information available at the time?
• How many trades and different market conditions does the test cover?
• What happened in the worst drawdown and later walk-forward windows?
• Would plausible fees, spread, slippage, and execution delay erase the result?
• How many forward paper trades exist, and are any broker executions independently verified?

Frequently asked questions

Sources for assessing backtests

• SEC Investor Bulletin: Performance Claims
• Standards Board for Alternative Investments: Backtesting questions
• QuantConnect: Reconciling backtests with live observations

Continue exploring CommonQuant

• CommonQuant home
• turn ideas into stock alerts
• XRP signal example and checks
• AI stock analysis
• public strategy track record
• research methodology and limits
• trading and execution disclosure
• news-based trading ideas
• MACD indicator guide
• CommonQuant vs TrendSpider
• CommonQuant vs Composer

Put your thesis through the research pipeline

Start free with no credit card. Review every strategy before activating it; CommonQuant sends signals and does not execute trades.