mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-07-31 02:47:44 +00:00
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.
69 lines
1.8 KiB
Plaintext
69 lines
1.8 KiB
Plaintext
#!meta
|
|
|
|
{"kernelInfo":{"defaultKernelName":"csharp","items":[{"name":"csharp","languageName":"csharp"},{"name":"fsharp","languageName":"F#","aliases":["f#","fs"]},{"name":"html","languageName":"HTML"},{"name":"http","languageName":"HTTP"},{"name":"javascript","languageName":"JavaScript","aliases":["js"]},{"name":"mermaid","languageName":"Mermaid"},{"name":"pwsh","languageName":"PowerShell","aliases":["powershell"]},{"name":"value"}]}}
|
|
|
|
#!csharp
|
|
|
|
// Reference the library
|
|
#r "..\..\bin\QuanTAlib.dll"
|
|
|
|
using QuanTAlib;
|
|
using System.IO;
|
|
|
|
// 1. Setup: Use existing CSV file
|
|
// CsvFeed expects a CSV with header: timestamp,open,high,low,close,volume
|
|
// Timestamp format: YYYY-MM-DD
|
|
|
|
string csvPath = "daily_IBM.csv";
|
|
Console.WriteLine($"Using CSV file: {csvPath}");
|
|
|
|
#!csharp
|
|
|
|
// 2. Initialize CsvFeed
|
|
// The feed loads the data and prepares it for streaming
|
|
|
|
var feed = new CsvFeed(csvPath);
|
|
Console.WriteLine("CsvFeed initialized.");
|
|
|
|
#!csharp
|
|
|
|
// 3. Streaming Data
|
|
// Simulate processing historical data bar by bar
|
|
|
|
Console.WriteLine("\nStreaming data (first 5 bars):");
|
|
int count = 0;
|
|
bool isNew = true;
|
|
|
|
// Get first bar
|
|
var bar = feed.Next(isNew: true);
|
|
|
|
while (isNew && count < 5)
|
|
{
|
|
count++;
|
|
Console.WriteLine($" Bar {count}: {bar}");
|
|
|
|
// Get next bar
|
|
bar = feed.Next(ref isNew);
|
|
}
|
|
|
|
Console.WriteLine($"Streamed {count} bars.");
|
|
|
|
#!csharp
|
|
|
|
// 4. Batch Fetching
|
|
// Retrieve a specific range of data
|
|
|
|
Console.WriteLine("\nBatch fetching:");
|
|
// Using a date range present in daily_IBM.csv (July 2025)
|
|
long startTime = new DateTime(2025, 7, 8).Ticks;
|
|
var interval = TimeSpan.FromDays(1);
|
|
|
|
// Fetch 3 bars starting from July 8th, 2025
|
|
var batch = feed.Fetch(5, startTime, interval);
|
|
|
|
Console.WriteLine($"Fetched {batch.Count} bars:");
|
|
foreach (var b in batch)
|
|
{
|
|
Console.WriteLine($" {b}");
|
|
}
|