๐ Description
The Scenarios panel models a distribution of potential future P&L outcomes by sampling random historical market windows and projecting forward from todayโs state.
It provides an intuitive, data-driven way to visualize how a trade could evolve across thousands of realistic market paths โ while preserving volatility clustering, correlations, and short-term momentum observed in history.
๐๏ธ Interactive Controls
| Control | Description |
|---|---|
| Number of Scenarios | Defines how many random historical windows (X) are drawn to generate simulated paths. |
| Forecast Horizon | Sets the number of days (Y) each scenario projects into the future. |
| IVol Drift | Additive drift applied to implied volatility levels during the projection. Positive = vol expansion; negative = vol crush. |
| Flatprice Drift | Additive drift applied to the underlyingโs expected move. Positive = bullish bias; negative = bearish bias. |
| Days in Trade (Slider) | Interactive slider to focus on a specific day in the forecast horizon and inspect the P&L distribution for that holding period. |
๐ Chart Components
P&L Fan Chart (Left Panel)
Solid Lines: Median and ยฑ2ฯ, ยฑ3ฯ envelopes of P&L across all simulated paths.
Vertical Axis: Profit & Loss ($).
Horizontal Axis: Days in Trade.
Interactive Cursor: Hover or slide to reveal per-day summary statistics.
Distribution Histogram (Right Panel)
Displays the P&L distribution on the selected day from all simulated paths.
Stats Panel:
Count Positive / Count Negative
% Positive (win-rate)
Min / Median / Mean / Max P&L
๐ Data & Methodology
Historical Sampling: Random contiguous blocks of Y days are drawn from historical daily moves (returns and iv moves).
Path Construction: Each block is compounded from the current underlying or flat price, with optional drift adjustments.
Re-valuation: The selected trade or strategy is repriced along each simulated path using the current volatility surface adjusted by drift parameters.
Aggregation: P&L results across all paths are summarized into mean, variance bands, and distribution statistics.
Sampling contiguous windows preserves short-term correlation and volatility clustering that pure random shuffles would lose โ producing more realistic outcomes.
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