Vwap test isolated fix (#1)

Author: celsisdurham

Cargo.toml

@@ -1,13 +1,13 @@
 [package]
 name = "ta-lib-in-rust"
-version = "1.0.5"
+version = "1.0.6"
 edition = "2021"
 description = "A library of technical indicators for financial analysis, similar to TA-Lib"
 authors = ["Celsis Durham <[email protected]>"]
 license = "MIT"
-repository = "https://github.com/rustic-ml/ta-lib-in-rust"
+repository = "https://github.com/rustic-ml/RusTaLib"
 # Add homepage, often same as repository if no separate project page
-homepage = "https://github.com/rustic-ml/ta-lib-in-rust"
+homepage = "https://github.com/rustic-ml/RusTaLib"
 documentation = "https://docs.rs/ta-lib-in-rust"
 readme = "README.md"
 keywords = ["finance", "technical-analysis", "trading", "indicators", "stocks"]
@@ -24,23 +24,23 @@ exclude = [
     ".gitignore",
     "Cargo.lock",
     "images_processed.png",
-    "tag_version.sh",
 
     # You can add more patterns:
     # "another_folder/",
     # "*.log",             # Exclude all .log files
 ]
 
 [dependencies]
-polars = { version = "0.46.0", features = ["lazy", "strings", "temporal", "rolling_window"] }
+polars = { version = "0.47.1", features = ["lazy", "strings", "temporal", "rolling_window", "parquet", "dtype-categorical", "dtype-struct"] }
 chrono = "0.4.34"
 thiserror = "2.0.11" # Consider updating if needed, check compatibility
 ndarray = "0.16.1" # Consider updating if needed, check compatibility
 serde = { version = "1.0", features = ["derive"] }
-rand = "0.8.5"
+rand = "0.9.1"
 
 [dev-dependencies]
 approx = "0.5.1"
+tempfile = "3.10.1"
 
 # General examples
 [[example]]
@@ -82,6 +82,14 @@ path = "examples/moving_averages_basic.rs"
 name = "rsi_basic"
 path = "examples/rsi_basic.rs"
 
+[[example]]
+name = "vwap_basic"
+path = "examples/vwap_basic.rs"
+
 [[example]]
 name = "working_with_multi_stock_data"
-path = "examples/working_with_multi_stock_data.rs"
+path = "examples/working_with_multi_stock_data.rs"
+
+[[example]]
+name = "file_reading_example"
+path = "examples/file_reading_example.rs"

README.md

@@ -1,6 +1,6 @@
 # RusTalib, the Crustacean Financial Analyst 🦀
 
-Meet **Rustalib**, your steadfast crustacean companion for navigating the currents of financial markets! This comprehensive Rust library, `ta-lib-in-rust`, provides a powerful toolkit for calculating technical indicators and building trading strategies, all powered by the high-performance [Polars](https://pola.rs/) DataFrame library.
+Meet **Rustalib**, your steadfast crustacean companion for navigating the currents of financial markets! This comprehensive Rust library, `ta-lib-in-rust`, provides a powerful toolkit for calculating technical indicators, all powered by the high-performance [Polars](https://pola.rs/) DataFrame library.
 
 Whether you're charting, backtesting, or building live trading systems, Rustalib is here to help you process market data with speed and precision.
 
@@ -11,11 +11,11 @@ Whether you're charting, backtesting, or building live trading systems, Rustalib
 
 ## Project Overview
 
-**Technical Indicators** aims to provide a robust, extensible, and efficient toolkit for quantitative finance, algorithmic trading, and data science in Rust. The library is designed for:
+**ta-lib-in-rust** provides a robust, extensible, and efficient toolkit for quantitative finance, algorithmic trading, and data science in Rust. The library is designed for:
 - **Fast, vectorized computation** using Polars DataFrames
 - **Easy integration** with modern Rust data workflows
-- **Modular design**: Use only the indicators or strategies you need
-- **Extensibility**: Add your own indicators or strategies easily
+- **Modular design**: Use only the indicators you need
+- **Extensibility**: Add your own indicators easily
 
 Whether you are backtesting, researching, or building production trading systems, this crate offers a solid foundation for technical analysis in Rust.
 
@@ -24,7 +24,6 @@ Whether you are backtesting, researching, or building production trading systems
 ## Features
 
 - **Wide range of indicators**: Moving averages, oscillators, volatility, volume, trend, momentum, and more
-- **Strategy modules**: Combine indicators into rule-based trading strategies
 - **Convenience functions**: Add a suite of indicators to your DataFrame in one call
 - **CSV and DataFrame workflows**: Read, process, and save data efficiently
 - **Well-documented and tested**
@@ -37,51 +36,6 @@ Whether you are backtesting, researching, or building production trading systems
 - **Volume**: On-Balance Volume (OBV), Chaikin Money Flow (CMF)
 - **Other**: Price returns, daily range, lagged prices, rolling returns/volatility, cyclical time features
 
-### Planned/Upcoming
-- Average Directional Index (ADX)
-- Rate of Change (ROC)
-
----
-
-## Feature Selection
-
-This crate supports feature selection, allowing you to include only the parts you need:
-
-### Types of Features
-- **indicators**: Only include technical indicator calculations
-- **strategies**: Only include trading strategies
-- **all**: Include both indicators and strategies (default)
-
-### How to Choose Features in Cargo.toml
-
-You can enable features in your `Cargo.toml` like this:
-
-```toml
-[dependencies]
-ta-lib-in-rust = { version = "x.y.z", features = ["strategies"] } # Only strategies
-ta-lib-in-rust = { version = "x.y.z", features = ["indicators"] } # Only indicators
-ta-lib-in-rust = { version = "x.y.z", features = ["all"] }        # Everything (default)
-```
-
-If you omit the `features` key, the default is `all`.
-
-### Programmatic Feature Selection
-
-You can also select features at runtime using the `FeatureSelection` enum and `select_features` function:
-
-```rust
-use ta_lib_in_rust::{FeatureSelection, select_features};
-let mut df = ...; // your Polars DataFrame
-// To add only indicators:
-let result = select_features(&mut df, FeatureSelection::Indicators);
-// To run a strategy:
-let result = select_features(&mut df, FeatureSelection::Strategy { strategy_name: "daily_1", params: None });
-// To do both:
-let result = select_features(&mut df, FeatureSelection::All { strategy_name: "daily_1", params: None });
-```
-
-See the crate documentation for more details and examples.
-
 ---
 
 ## Installation
@@ -91,7 +45,6 @@ Add to your `Cargo.toml`:
 ```toml
 [dependencies]
 ta-lib-in-rust = "*" # Or specify a version
-tokio = { version = "1", features = ["full"] } # If using async examples
 polars = { version = "0.46", features = ["lazy", "dtype-full"] }
 ```
 
@@ -138,50 +91,7 @@ fn main() -> PolarsResult<()> {
 }
 ```
 
-### 3. Run a Strategy and Analyze Results
-```rust
-use polars::prelude::*;
-use ta_lib_in_rust::strategy::minute::enhanced_minute_strategy::{
-    run_strategy, calculate_performance, StrategyParams
-};
-
-fn main() -> PolarsResult<()> {
-    let df = CsvReadOptions::default()
-        .with_has_header(true)
-        .try_into_reader_with_file_path(Some("examples/AAPL_minute_ohlcv.csv".into()))?
-        .finish()?;
-    
-    // Note: This library expects lowercase column names (open, high, low, close, volume)
-    // If your CSV has uppercase names, you need to rename them:
-    let df = df.lazy()
-        .select([
-            col("Open").alias("open"),
-            col("High").alias("high"),
-            col("Low").alias("low"),
-            col("Close").alias("close"),
-            col("Volume").cast(DataType::Float64).alias("volume"),
-        ])
-        .collect()?;
-        
-    let params = StrategyParams::default();
-    let signals = run_strategy(&df, &params)?;
-    let (
-        final_value, total_return, num_trades, win_rate, max_drawdown, profit_factor, avg_profit_per_trade
-    ) = calculate_performance(
-        df.column("close")?,
-        &signals.buy_signals,
-        &signals.sell_signals,
-        &signals.stop_levels,
-        &signals.target_levels,
-        10000.0,
-        true,
-    );
-    println!("Final Value: ${:.2}, Total Return: {:.2}%", final_value, total_return);
-    Ok(())
-}
-```
-
-### 4. Reading Data from CSV and Saving Results
+### 3. Reading Data from CSV and Saving Results
 ```rust
 let df = CsvReadOptions::default()
     .with_has_header(true)
@@ -201,7 +111,7 @@ let mut df = df.lazy()
     ])
     .collect()?;
 
-// ... apply indicators or strategies ...
+// ... apply indicators ...
 CsvWriter::new(std::io::BufWriter::new(std::fs::File::create("results.csv")?))
     .finish(&mut df)?;
 ```
@@ -212,25 +122,22 @@ CsvWriter::new(std::io::BufWriter::new(std::fs::File::create("results.csv")?))
 
 See the [`examples/`](examples/) directory for:
 - **Basic indicator usage** (SMA, EMA, RSI, MACD, Bollinger Bands, etc.)
-- **Strategy backtests** (minute and daily)
 - **CSV workflows** for real-world data
+- **Multi-stock analysis** with cross-asset comparisons
 - **Saving and analyzing results**
 
 ## Important Notes
 
 ### Column Name Sensitivity
 This library expects lowercase column names (`open`, `high`, `low`, `close`, `volume`) in DataFrames. When working with CSVs that might have different case formats (e.g., `Open`, `High`, etc.), make sure to rename the columns using Polars' selection and aliasing capabilities as shown in the examples above.
 
-### Working with Multiple Stock Data Files
-The examples directory contains sample code for running strategies on multiple stocks (AAPL, GOOGL, MSFT). You can use these as templates for your own multi-asset analysis.
-
 ---
 
 ## Contributing
 
 Contributions are welcome! Please:
 - Open issues for bugs, questions, or feature requests
-- Submit pull requests for new indicators, strategies, or improvements
+- Submit pull requests for new indicators or improvements
 - Follow Rust best practices and add tests/docs for new code
 
 ---

examples/README.md

@@ -35,6 +35,12 @@ Options market specific trading strategies:
 - `options/volatility_strategies.rs` - Demonstrates volatility-based options strategies
 - `options/delta_neutral.rs` - Shows delta-neutral options strategies implementation
 
+## Multi-Asset Analysis Examples
+
+Examples for processing and analyzing multiple assets:
+
+- `working_with_multi_stock_data.rs` - Demonstrates how to load, process, and compare technical indicators across multiple stocks. Shows how to handle data from different CSV sources, standardize column formats, calculate key technical indicators, and perform cross-stock comparison analysis.
+
 ## Running the Examples
 
 To run any example, use the following command from the project root:
@@ -48,6 +54,7 @@ For instance:
 ```bash
 cargo run --example general/basic_indicators
 cargo run --example stock/trend_following
+cargo run --example working_with_multi_stock_data
 ```
 
 ## Notes for Real-World Application