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Miha Kralj
2026-02-27 07:48:12 -08:00
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lib/feeds/IFeed.md
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# IFeed Interface
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Feed |
| **Inputs** | OHLCV bar (TBar) |
| **Parameters** | None |
| **Outputs** | Single series (IFeed) |
| **Output range** | Varies (see docs) |
| **Warmup** | 1 bar |
### TL;DR
- `IFeed` defines the standard contract for all data feeds in QuanTAlib, ensuring consistent behavior across different data sources (synthetic, file-...
- No configurable parameters; computation is stateless per bar.
- Output range: Varies (see docs).
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
`IFeed` defines the standard contract for all data feeds in QuanTAlib, ensuring consistent behavior across different data sources (synthetic, file-based, or live API).
## Key Concepts
lib/feeds/csvfeed/CsvFeed.md
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# CsvFeed Class
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Feed |
| **Inputs** | OHLCV bar (TBar) |
| **Parameters** | `filePath` |
| **Outputs** | Single series (CsvFeed) |
| **Output range** | Varies (see docs) |
| **Warmup** | 1 bar |
### TL;DR
- `CsvFeed` is a file-based feed implementation that loads historical OHLCV data from CSV files.
- Parameterized by `filepath`.
- Output range: Varies (see docs).
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
`CsvFeed` is a file-based feed implementation that loads historical OHLCV data from CSV files. It supports both streaming access (simulating real-time playback) and batch retrieval.
## Key Features
lib/feeds/gbm/GBM.md
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# GBM Class
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Feed |
| **Inputs** | OHLCV bar (TBar) |
| **Parameters** | None |
| **Outputs** | Single series (GBM) |
| **Output range** | Varies (see docs) |
| **Warmup** | 1 bar |
### TL;DR
- `GBM` (Geometric Brownian Motion) is a synthetic data generator that simulates realistic financial price movements.
- No configurable parameters; computation is stateless per bar.
- Output range: Varies (see docs).
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
`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.
## Key Features