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v0.8.6: update indicator docs, ndepend tooling, ALMA refactor, gitignore cleanup

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Miha Kralj
2026-03-13 13:46:52 -07:00
parent e3e9555fc1
commit c75135ab14
402 changed files with 2222 additions and 1779 deletions
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lib/feeds/IFeed.md
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@@ -9,11 +9,12 @@
| **Output range** | Varies (see docs) |
| **Warmup** | 1 bar |
- `IFeed` defines the standard contract for all data feeds in QuanTAlib, ensuring consistent behavior across different data sources (synthetic, file-...
- `IFeed` defines the standard contract for all data feeds in QuanTAlib, ensuring consistent behavior across different data sources (synthetic, file-based, or live API).
- 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.
- Feeds are the entry point of any indicator chain — all indicators subscribe to a feed or to another indicator's output.
- Common feed implementations include synthetic (GBM), file-based (CSV), and live API sources (Alpha Vantage).
- For backtesting, pair feeds with any trend, momentum, or volatility indicator to simulate streaming bar-by-bar processing.
`IFeed` defines the standard contract for all data feeds in QuanTAlib, ensuring consistent behavior across different data sources (synthetic, file-based, or live API).
lib/feeds/csvfeed/CsvFeed.md
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@@ -10,9 +10,6 @@
| **Warmup** | 1 bar |
- `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.
@@ -84,4 +81,4 @@ while (true)
```csharp
long startTime = new DateTime(2024, 1, 1).Ticks;
var batch = feed.Fetch(10, startTime, TimeSpan.FromDays(1));
var batch = feed.Fetch(10, startTime, TimeSpan.FromDays(1));
lib/feeds/gbm/GBM.md
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@@ -11,8 +11,6 @@
- `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.
@@ -83,4 +81,4 @@ long startTime = DateTime.UtcNow.Ticks;
var interval = TimeSpan.FromMinutes(1);
// Generate 1000 bars
var history = gbm.Fetch(1000, startTime, interval);
var history = gbm.Fetch(1000, startTime, interval);