Strategy Examples

This page provides practical strategy recipes that you can build in Stratifyre using the visual rule builder. Each example describes the trading idea, the exact rules to configure, and tips for getting the most out of the strategy.

These are starting points, not finished products. Every strategy should be backtested and refined for your specific instruments, timeframes, and risk tolerance.

SMA Crossover (trend following)

The idea: Follow the trend by buying when a faster moving average crosses above a slower one, and selling when it crosses back below. This is one of the most widely used strategy concepts in all of trading.

Instruments: Any trending instrument — stocks, ETFs, futures, or crypto.

Timeframe: Daily (best for swing trading) or 1-hour (for shorter-term trading).

Entry rule — Go long

Field	Setting
Condition 1 LHS	SMA (Period: 20)
Condition 1 Operator	Crossing Up
Condition 1 RHS	SMA (Period: 50)
Action	Place Order — Buy, Market
Quantity	Fixed (for example, 100 shares) or expression: round(account.equity * 0.05 / price)
Frequency	Once per period (Daily)

Exit rule — Close long

Field	Setting
Condition 1 LHS	SMA (Period: 20)
Condition 1 Operator	Crossing Down
Condition 1 RHS	SMA (Period: 50)
Action	Close Position — Flatten
Frequency	No limit

Stop-loss rule

Field	Setting
Condition 1 LHS	Position Return %
Condition 1 Operator	Less Than
Condition 1 RHS	-3
Action	Close Position — Flatten
Frequency	No limit

Tip

Refinement ideas: Try EMA instead of SMA for faster signals. Add a trend-strength filter (ADX > 25) to avoid whipsaws in sideways markets. Experiment with different period combinations like 10/30 or 50/200.

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RSI Mean Reversion

The idea: Buy when a stock has been oversold (RSI below 30) and sell when it recovers. Mean-reversion strategies profit from the tendency of prices to bounce back after short-term extremes.

Instruments: Large-cap stocks or ETFs with a history of mean-reverting behavior (for example, SPY, QQQ).

Timeframe: Daily.

Entry rule — Buy the dip

Field	Setting
Condition 1 LHS	RSI (Period: 14)
Condition 1 Operator	Less Than
Condition 1 RHS	30
Condition 2 LHS	Price
Condition 2 Operator	Greater Than
Condition 2 RHS	SMA (Period: 200)
Action	Place Order — Buy, Market
Quantity	Expression: round(account.equity * 0.05 / price)
Frequency	Once

The second condition (price above the 200-day SMA) acts as a trend filter — you only buy dips in instruments that are in a longer-term uptrend.

Exit rule — Take profit

Field	Setting
Condition 1 LHS	RSI (Period: 14)
Condition 1 Operator	Greater Than
Condition 1 RHS	50
Condition 2 LHS	Position Quantity
Condition 2 Operator	Greater Than
Condition 2 RHS	0
Action	Close Position — Flatten
Frequency	No limit

Stop-loss rule

Field	Setting
Condition 1 LHS	Position Return %
Condition 1 Operator	Less Than
Condition 1 RHS	-2
Action	Close Position — Flatten
Frequency	No limit

Tip

Refinement ideas: For a more aggressive version, use RSI(2) (2-period RSI) which generates more frequent signals. You can also add a volume filter to only buy dips on above-average volume, suggesting institutional interest.

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Bollinger Band Squeeze Breakout

The idea: Bollinger Bands contract when volatility drops (the “squeeze”) and expand when volatility returns. This strategy waits for a squeeze and then enters when price breaks out of the bands, betting that the volatility expansion will lead to a sustained move.

Instruments: Stocks, futures, or crypto with periodic volatility cycles.

Timeframe: Daily or 1-hour.

Setup rule — Detect the squeeze

First, create a variable to track whether a squeeze is happening:

Field	Setting
Condition 1 LHS	Expression: (BBANDS(20).upper - BBANDS(20).lower) / BBANDS(20).middle * 100
Condition 1 Operator	Less Than
Condition 1 RHS	4
Action	Set Variable — Name: squeezeActive, Value: 1, Scope: Local
Frequency	No limit

This fires whenever band width drops below 4% of the middle band, indicating low volatility.

Entry rule — Breakout long

Field	Setting
Condition 1 LHS	vars.squeezeActive
Condition 1 Operator	Equals
Condition 1 RHS	1
Condition 2 LHS	Price
Condition 2 Operator	Greater Than
Condition 2 RHS	Bollinger Bands (Period: 20, StdDev: 2) — Upper Band
Action	Place Order — Buy, Market
Quantity	Expression: round(account.equity * 0.03 / price)
Frequency	Once

Exit rule — Return to middle

Field	Setting
Condition 1 LHS	Price
Condition 1 Operator	Less Than
Condition 1 RHS	Bollinger Bands (Period: 20) — Middle Band
Condition 2 LHS	Position Quantity
Condition 2 Operator	Greater Than
Condition 2 RHS	0
Action	Close Position — Flatten
Frequency	No limit

Stop-loss rule

Field	Setting
Condition 1 LHS	Price
Condition 1 Operator	Less Than
Condition 1 RHS	Bollinger Bands (Period: 20) — Lower Band
Action	Close Position — Flatten
Frequency	No limit

Note

You can also build the short-side version of this strategy by entering when price breaks below the lower band during a squeeze and exiting at the middle band.

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Daily Range Breakout

The idea: Enter a position when today’s price breaks above yesterday’s high (long) or below yesterday’s low (short), during the first hour of the trading session. This captures the initial momentum of the day.

Instruments: Liquid stocks, futures (ES, NQ), or forex pairs.

Timeframe: 5 minutes (with daily reference data).

Entry rule — Breakout long

Field	Setting
Condition 1 LHS	Price
Condition 1 Operator	Greater Than
Condition 1 RHS	Expression: bars["D"].high[1] (yesterday’s high)
Condition 2 LHS	time.min_since_rth_open
Condition 2 Operator	Less Than
Condition 2 RHS	60
Condition 3 LHS	Volume
Condition 3 Operator	Greater Than
Condition 3 RHS	Expression: SMA(20, "volume") * 1.5
Action	Place Order — Buy, Bracket (Take Profit: order.entry_price * 1.01, Stop Loss: order.entry_price * 0.995)
Quantity	Expression: round(account.equity * 0.02 / (price * 0.005))
Frequency	Once per period (Daily)

The time filter ensures you only trade breakouts in the first hour. The volume filter confirms the breakout has participation. The bracket order automatically sets a 1% take-profit and 0.5% stop-loss.

End-of-day exit

Field	Setting
Condition 1 LHS	time.min_since_rth_open
Condition 1 Operator	Greater Than
Condition 1 RHS	360
Condition 2 LHS	Position Quantity
Condition 2 Operator	Not Equals
Condition 2 RHS	0
Action	Close Position — Flatten
Frequency	Once per period (Daily)

This closes any remaining position 6 hours after the open, keeping the strategy as a day-trading system.

Tip

Refinement ideas: Add a daily trend filter (for example, only take long breakouts when the daily SMA(50) is above the daily SMA(200)). You can also vary the time window — some traders prefer the first 30 minutes or the first 2 hours.

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ATR-Based Position Sizing and Trailing Stop

The idea: Use the Average True Range (ATR) to dynamically size positions and set trailing stops. This is not a standalone strategy but a risk-management framework you can add to any strategy.

Applicable to: Any entry strategy. This example uses a simple RSI entry for illustration.

Entry rule with ATR-based sizing

Field	Setting
Condition 1 LHS	RSI (Period: 14)
Condition 1 Operator	Less Than
Condition 1 RHS	30
Action	Place Order — Buy, Market
Quantity	Expression: round(account.equity * 0.02 / ATR(14))
Frequency	Once

This calculates quantity so that if price moves one ATR against you, you lose 2% of equity. When ATR is high (volatile market), you trade fewer shares. When ATR is low (calm market), you trade more.

Record entry price

Add a second action to the entry rule (or create a separate rule with the same conditions):

Field	Setting
Action	Set Variable — Name: entryPrice, Value: price, Scope: Local

Set initial trailing stop

Field	Setting
Action	Set Variable — Name: trailingStop, Value: price - ATR(14) * 1.5, Scope: Local

Update trailing stop

Field	Setting
Condition 1 LHS	Position Quantity
Condition 1 Operator	Greater Than
Condition 1 RHS	0
Condition 2 LHS	Expression: price - ATR(14) * 1.5
Condition 2 Operator	Greater Than
Condition 2 RHS	vars.trailingStop
Action	Set Variable — Name: trailingStop, Value: price - ATR(14) * 1.5, Scope: Local
Frequency	No limit

This rule only updates the trailing stop when the new value is higher than the current one, so the stop only moves up (for long positions), never down.

Exit on trailing stop

Field	Setting
Condition 1 LHS	Price
Condition 1 Operator	Less Than
Condition 1 RHS	vars.trailingStop
Condition 2 LHS	Position Quantity
Condition 2 Operator	Greater Than
Condition 2 RHS	0
Action	Close Position — Flatten
Frequency	No limit

Tip

The ATR multiplier (1.5 in this example) controls how tight or loose the trailing stop is. A multiplier of 1.0 creates a tight stop that triggers often; 3.0 creates a loose stop that gives the trade more room. Backtest different values to find the right balance for your instruments.

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Multi-Timeframe Trend + Pullback

The idea: Use the daily chart to confirm a bullish trend, then use the 15-minute chart to buy pullbacks. This combines the reliability of a higher-timeframe trend with the precision of a lower-timeframe entry.

Instruments: Stocks, ETFs, or futures.

Timeframe: 15 minutes (primary), with daily references.

Entry rule — Buy the pullback

Field	Setting
Condition 1 LHS	SMA (Period: 50, Timeframe: Daily)
Condition 1 Operator	Greater Than
Condition 1 RHS	SMA (Period: 200, Timeframe: Daily)
Condition 2 LHS	RSI (Period: 14)
Condition 2 Operator	Less Than
Condition 2 RHS	35
Condition 3 LHS	Price
Condition 3 Operator	Greater Than
Condition 3 RHS	SMA (Period: 20)
Condition 4 LHS	session.is_rth
Condition 4 Operator	Equals
Condition 4 RHS	True
Action	Place Order — Buy, Market
Quantity	Expression: round(account.equity * 0.02 / ATR(14))
Frequency	Once per period (Daily)

What this does:

• Condition 1 confirms the daily trend is bullish (50-day SMA above 200-day SMA).
• Condition 2 finds a short-term pullback on the 15-minute chart (RSI oversold).
• Condition 3 confirms price is still holding above the 15-minute 20-period SMA (not in freefall).
• Condition 4 ensures you only trade during regular hours.
• Frequency is once per day, so you get at most one entry per day.

Exit rule — RSI recovery

Field	Setting
Condition 1 LHS	RSI (Period: 14)
Condition 1 Operator	Greater Than
Condition 1 RHS	65
Condition 2 LHS	Position Quantity
Condition 2 Operator	Greater Than
Condition 2 RHS	0
Action	Close Position — Flatten
Frequency	No limit

Stop-loss rule

Field	Setting
Condition 1 LHS	Price
Condition 1 Operator	Less Than
Condition 1 RHS	Expression: bars["D"].low[1] (yesterday’s low)
Action	Close Position — Flatten
Frequency	No limit

Using the previous day’s low as a stop-loss level provides a natural support reference from the higher timeframe.

Note

This strategy demonstrates how multi-timeframe analysis works in practice. The daily conditions act as a filter, and the 15-minute conditions provide the trigger. See Multi-Timeframe Strategies for more patterns and details.

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Next steps

These examples cover the most common strategy styles, but Stratifyre’s rule builder supports far more complex approaches. Here are some ways to go further:

• Combine elements from multiple examples. For instance, use ATR-based position sizing (from the ATR example) with the Bollinger Band squeeze entry.
• Add more filters. Volume confirmation, time-of-day filters, day-of-week filters, and market-regime detection can all improve signal quality.
• Try short-side versions. Most of the examples above focus on long entries. Flip the conditions to create short strategies (for example, RSI above 70 instead of below 30).
• Apply to multiple instruments. Take any single-instrument strategy and apply it across a basket using multi-instrument features.
• Use the AI Strategy Builder to quickly generate variations based on natural-language descriptions.

Caution

All examples in this guide are for educational purposes. Past performance in backtesting does not guarantee future results. Always test thoroughly across different market conditions, account for transaction costs, and never risk more than you can afford to lose.