Strategy #0000000093 · Price-pattern recognition
Chart patterns and breakout scenarios
Choose a pattern, fit threshold, breakout direction, target, stop and cost to calculate a scenario expectancy.
Head-and-shoulders, triangles, rectangles, double tops/bottoms and broadening patterns can be algorithmically defined from highs and lows. Studies test conditional returns after pattern completion, but definitions, windows, smoothing and selection bias matter. This simulator does not detect charts; users enter fit, breakout direction and target/stop scenarios.
Fit is not a success probability; no high/low series is loaded and no empirical pattern return is estimated.
Three ways in
Understand the story. Then inspect the risk.
Start with a familiar situation, check the payoff, then test how the numbers change.
The idea · in two minutes
How do pattern fit and breakout direction change a target-versus-stop scenario?
Head-and-shoulders, triangles, rectangles, double tops/bottoms and broadening patterns can be algorithmically defined from highs and lows. Studies test conditional returns after pattern completion, but definitions, windows, smoothing and selection bias matter. This simulator does not detect charts; users enter fit, breakout direction and target/stop scenarios.
Choose a pattern, fit threshold, breakout direction, target, stop and cost to calculate a scenario expectancy.
Fit is not a success probability; no high/low series is loaded and no empirical pattern return is estimated.
- Input assumptionsChoose a pattern, fit threshold, breakout direction, target, stop and cost to calculate a scenario expectancy.
- Model outputFit is not a success probability; no high/low series is loaded and no empirical pattern return is estimated.
Illustrative research examples only. They are not live signals or a recommendation.
The mechanics · payoff and exposure
E[R]=p\cdot Target-(1-p)\cdot Stop-C
p is a user-entered pattern-fit proxy, not a calibrated probability; target and stop are percentages.
Greeks: the directional intuition
- 模型
- Measures sensitivity to implied volatility.
- 尾部
- Measures sensitivity to implied volatility.
- 執行
- Measures sensitivity to implied volatility.
E[R]=p\cdot Target-(1-p)\cdot Stop-C
Practical context · published observations
Chart patterns and breakout scenarios: read the record with its caveats.
The chart uses the published figures or explicitly labelled study inputs below. A historical index or model replay is not the same as an investor’s realised account return.
Fit is not a success probability; no high/low series is loaded and no empirical pattern return is estimated. Source ↗ · Methodology ↗
Change assumptions and inspect the scenario
Inputs illustrate the strategy mechanics; they are not live quotes, forecasts or investment advice.
Variants · same family, different risk levers
Related structures use different exposures and loss conditions.
Compare allocation, financing and payoff assumptions.
| Related strategy | Relationship | What changes | New cost or risk |
|---|---|---|---|
| Base structure | Scenario allocation | Head-and-shoulders, triangles, rectangles, double tops/bottoms and broadening patterns can be algorithmically defined from highs and lows. Studies test conditional returns after pattern completion, but definitions, windows, smoothing and selection bias matter. This simulator does not detect charts; users enter fit, breakout direction and target/stop scenarios. | Fit is not a success probability; no high/low series is loaded and no empirical pattern return is estimated. |
| Conservative version | Lower exposure | Reduce notional, leverage or multiplier. | May reduce upside participation. |
| Dynamic version | Periodic rebalance | Reset exposure by risk or regime. | Needs more trading and model governance. |
Fit is not a success probability; no high/low series is loaded and no empirical pattern return is estimated.
Read beyond the summary
References
Primary sources and research used to describe the strategy and its limits.