mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-21 20:18:05 +00:00
Miha Kralj
74b49d2bb4
- Introduced TBar struct for efficient OHLCV data representation. - Implemented TBarSeries class for high-performance collection of TBar instances using Structure of Arrays (SoA) layout. - Added TSeries class for time-series data management with zero-copy access. - Created TValue struct for time-value pairs with implicit conversions. - Defined IFeed interface for consistent data feed implementations. - Developed CsvFeed class for loading historical OHLCV data from CSV files. - Implemented GBM class for generating synthetic financial data using Geometric Brownian Motion. - Added Quantower project files for Averages indicator with necessary dependencies and configurations. - Included extensive usage examples and notebooks for TBar, TBarSeries, TSeries, TValue, and feed implementations.
68 lines
2.0 KiB
Markdown
68 lines
2.0 KiB
Markdown
# GBM Class
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`GBM` (Geometric Brownian Motion) is a synthetic data generator that simulates realistic financial price movements. It is useful for testing indicators, strategies, and system performance without relying on external data files.
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## Key Features
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- **Geometric Brownian Motion**: Uses the standard mathematical model for asset price dynamics.
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- **Configurable Parameters**: Control drift (trend) and volatility (noise).
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- **Stateless Design**: Minimal memory footprint; only maintains state needed for continuity.
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- **Dual Modes**: Supports both streaming (bar-by-bar) and batch generation.
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- **Intra-bar Updates**: Can simulate real-time price updates within a single bar.
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## Mathematical Model
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The price evolution follows the stochastic differential equation:
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$$ dS_t = \mu S_t dt + \sigma S_t dW_t $$
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Where:
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- $S_t$: Asset price at time $t$
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- $\mu$: Drift (expected return)
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- $\sigma$: Volatility (standard deviation of returns)
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- $W_t$: Wiener process (Brownian motion)
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## Class Definition
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```csharp
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public class GBM : IFeed
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{
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public GBM(double startPrice = 100.0, double mu = 0.05, double sigma = 0.2, TimeSpan? defaultTimeframe = null);
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public TBar Next(bool isNew = true);
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public TBarSeries Fetch(int count, long startTime, TimeSpan interval);
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}
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```
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## Usage
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### 1. Initialization
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```csharp
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// Default: Start at 100, 5% drift, 20% volatility
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var gbm = new GBM();
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// Custom: Start at 50, 10% drift, 50% volatility
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var volatileGbm = new GBM(startPrice: 50.0, mu: 0.10, sigma: 0.50);
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```
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### 2. Streaming Generation
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```csharp
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// Generate a new bar
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var bar = gbm.Next(isNew: true);
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// Simulate intra-bar updates (e.g., real-time ticks)
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for (int i = 0; i < 5; i++)
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{
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var updatedBar = gbm.Next(isNew: false);
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Console.WriteLine($"Update: {updatedBar.Close}");
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}
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```
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### 3. Batch Generation
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```csharp
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long startTime = DateTime.UtcNow.Ticks;
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var interval = TimeSpan.FromMinutes(1);
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// Generate 1000 bars
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var history = gbm.Fetch(1000, startTime, interval);
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