Turn Trading Ideas into Explicit, Testable Rules
The Strategy Builder is designed to help you define entries, exits, parameters and risk controls visually, then send them straight to a backtest.
Why explicit rules
If you cannot write it down, you cannot test it
"Buy the dip in a strong market" sounds clear until you try to test it. What counts as a dip? How strong is strong? When do you sell? The Strategy Builder is designed to force those answers into precise, readable conditions.
- IF / AND / OR blocks that read like plain sentences
- Separate exit and risk rules so no position is left without a way out
- Named parameters you can adjust without rewriting rules
- One click to backtest the exact version you are looking at
Product status: Quantzivo Studio is in development. Features described here reflect the planned scope and may change before release.
Planned capabilities
What you will be able to define
Rule-based strategies
Combine conditions with AND / OR logic into a complete, deterministic strategy.
Entry and exit rules
Define when to open, scale and close positions, including time-based exits.
Technical conditions
Trend, momentum, volatility, volume and price-level conditions from common indicators.
Strategy parameters
Expose lookbacks and thresholds as named inputs for controlled sensitivity tests.
Risk controls
Stop losses, take profits, position limits and exposure caps attached to every strategy.
Strategy versioning
Every saved change becomes a version you can compare, annotate and roll back to.
Visual configuration
Build and rearrange rule blocks visually, with a plain-language summary alongside.
Rule validation
Warnings for missing exits, conflicting conditions or indicators that need more history.
Educational example
An RSI pullback with a 200-day trend filter, in plain language
This is the kind of rule set the Strategy Builder is designed to express. It is a teaching example used to show structure, not a strategy we recommend or expect to be profitable.
| Component | Rule | What it means |
|---|---|---|
| Data | Daily bars, one instrument at a time | Each decision is made once per day using that day's completed bar. |
| Trend filter | Close > 200-day simple moving average | Only consider buying when price is above its long-term average. |
| Entry | RSI(14) < 30 AND trend filter is true | Buy after a short-term pullback, but only within a longer-term uptrend. |
| Fill timing | Next bar's open | The order is assumed to fill on the following open, never on the bar that triggered it. |
| Exit | RSI(14) > 55, or 10 bars in the trade | Sell once momentum recovers, or give up if it has not after two trading weeks. |
| Stop loss | 2% below entry price | Cap the loss on any single trade if the pullback keeps falling. |
| Position size | 5% of equity per trade | Limit how much any one trade can affect the portfolio. |
Why the trend filter?
Oversold readings in a falling market can stay oversold. The filter tests whether pullbacks behave differently when the long-term trend is up.
Why a time exit?
Without one, a losing trade can sit open indefinitely. A time limit keeps capital from being tied up in an idea that is not working.
What it leaves out
Gaps through the stop, costs, slippage and changing market regimes. A backtest has to model these before the result means anything.
Educational example only. This rule set illustrates how strategies are structured. It is not a recommendation to buy or sell any security, and no claim is made about how it would perform.
How it will work
From hypothesis to backtest in four steps
State the hypothesis
Note what you expect to happen and why, so the test has something to confirm or reject.
Add conditions
Choose indicators and thresholds for entries and exits, and set the data frequency.
Attach risk controls
Set stops, targets, position sizing and exposure limits before anything runs.
Save and backtest
Save a version and send it to the Backtesting Engine with costs and slippage applied.
Backtesting
Technical conditions
Planned building blocks for your rules
Condition families are grouped by what they measure. Each can be combined with others and tuned through parameters.
Direction
Trend
Is price generally rising or falling over the window you care about?
- Simple moving average
- Exponential moving average
- Crossovers
- Price vs. average
Speed
Momentum
How fast price has moved recently, and whether that move is stretched.
- RSI
- MACD
- Rate of change
- Stochastic
Range
Volatility
How much price typically moves, useful for filters and for sizing stops.
- ATR
- Bollinger Bands
- Rolling standard deviation
Participation
Volume
Whether a move is happening on unusually high or low trading activity.
- Volume vs. average
- On-balance volume
- Liquidity filters
Levels
Price structure
Breakouts and pullbacks relative to recent highs, lows and ranges.
- N-day high / low
- Gap size
- Percent from high
Timing
Time and calendar
Rules tied to holding periods, sessions or days rather than price.
- Bars in trade
- Day of week
- Session filters
Risk controls & parameters
Risk is part of the strategy, not an afterthought
The Builder is designed so a strategy cannot be saved without an exit path. Parameters are kept separate from logic, which makes it easy to test sensitivity and harder to tune a rule until it fits the past perfectly.
Stop-loss, take-profit and trailing-stop rules- Position sizing as a share of equity or volatility-based
- Limits on open positions and total exposure
- Parameter ranges for sensitivity tests, with an overfitting reminder
Strategy versioning
Keep an honest record of every change
Tweaking a rule after each disappointing backtest is one of the fastest routes to overfitting. Versioning is designed to make that process visible.
Saved versions
Each save captures rules, parameters and risk settings together.
Side-by-side diffs
See exactly which condition or threshold changed between two versions.
Change notes
Record why you made a change before you see the new result.
Linked results
Every backtest stays attached to the version that produced it.
Move to Python code
Paper trading
Algorithmic Trading with Python course
Strategy Builder FAQs
Common questions
That is the plan. Strategies built visually are designed to be exportable to the Coding Workspace as a Python starting point, so you can extend them when rules alone are not enough.
Studio is planned to include educational examples, like the RSI pullback above, to show how strategies are structured. They are learning material, not recommendations or signals.
It cannot prevent it, but it is designed to make it visible: versions record every change, parameters are tested as ranges rather than single values, and backtests can hold out data for out-of-sample checks.
No. Strategies can be backtested on historical data and run in simulated paper trading. Studio does not connect to brokers or execute real orders.
Strategy Builder
Want to see the Strategy Builder first?
Request a demo to hear about early previews and tell us which conditions and risk controls matter most to your research.