Updating project name.

Cargo.toml

@@ -1,5 +1,5 @@
 [package]
-name = "ta-lib-in-rust"
+name = "rusttalib"
 version = "1.0.6"
 edition = "2021"
 description = "A library of technical indicators for financial analysis, similar to TA-Lib"
@@ -8,7 +8,7 @@ license = "MIT"
 repository = "https://github.com/rustic-ml/RusTaLib"
 # Add homepage, often same as repository if no separate project page
 homepage = "https://github.com/rustic-ml/RusTaLib"
-documentation = "https://docs.rs/ta-lib-in-rust"
+documentation = "https://docs.rs/rusttalib"
 readme = "README.md"
 keywords = ["finance", "technical-analysis", "trading", "indicators", "stocks"]
 # Add relevant categories for discoverability

README.md

@@ -1,17 +1,17 @@
 # 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, 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, `rusttalib`, 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.
 
-![Technical Indicators Library Icon](images_processed.png) [![crates.io](https://img.shields.io/crates/v/ta-lib-in-rust.svg)](https://crates.io/crates/ta-lib-in-rust)
-[![docs.rs](https://docs.rs/ta-lib-in-rust/badge.svg)](https://docs.rs/ta-lib-in-rust)
+![Technical Indicators Library Icon](images_processed.png) [![crates.io](https://img.shields.io/crates/v/rusttalib.svg)](https://crates.io/crates/rusttalib)
+[![docs.rs](https://docs.rs/rusttalib/badge.svg)](https://docs.rs/rusttalib)
 
 ---
 
 ## Project Overview
 
-**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:
+**rusttalib** 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 you need
@@ -44,7 +44,7 @@ Add to your `Cargo.toml`:
 
 ```toml
 [dependencies]
-ta-lib-in-rust = "*" # Or specify a version
+rusttalib = "*" # Or specify a version
 polars = { version = "0.46", features = ["lazy", "dtype-full"] }
 ```
 
@@ -143,9 +143,9 @@ Contributions are welcome! Please:
 ---
 
 ## Links
-- [Crates.io](https://crates.io/crates/ta-lib-in-rust)
-- [Documentation (docs.rs)](https://docs.rs/ta-lib-in-rust)
-- [GitHub Repository](https://github.com/rustic-ml/ta-lib-in-rust)
+- [Crates.io](https://crates.io/crates/rusttalib)
+- [Documentation (docs.rs)](https://docs.rs/rusttalib)
+- [GitHub Repository](https://github.com/rustic-ml/rusttalib)
 
 ## License
 

docs/csv_guide.md

@@ -1,6 +1,6 @@
-# Working with CSV Data in ta-lib-in-rust
+# Working with CSV Data in rusttalib
 
-This guide explains how to effectively work with CSV data when using the ta-lib-in-rust library.
+This guide explains how to effectively work with CSV data when using the rusttalib library.
 
 ## CSV Format Requirements
 

examples/bollinger_bands_basic.rs

@@ -1,5 +1,5 @@
 use polars::prelude::*;
-use ta_lib_in_rust::indicators::volatility::calculate_bollinger_bands;
+use rusttalib::indicators::volatility::calculate_bollinger_bands;
 
 fn main() -> Result<(), PolarsError> {
     // Create a simple price series with some movement

examples/file_reading_example.rs

@@ -1,8 +1,6 @@
 use polars::prelude::*;
+use rusttalib::util::file_utils::{read_csv, read_csv_default, read_financial_data, read_parquet};
 use std::error::Error;
-use ta_lib_in_rust::util::file_utils::{
-    read_csv, read_csv_default, read_financial_data, read_parquet,
-};
 
 fn main() -> Result<(), Box<dyn Error>> {
     println!("======= Reading Financial Data Files Example =======\n");

examples/general/basic_indicators.rs

@@ -1,5 +1,5 @@
 use polars::prelude::*;
-use ta_lib_in_rust::indicators::{
+use rusttalib::indicators::{
     moving_averages::{calculate_ema, calculate_sma, calculate_wma},
     oscillators::{calculate_macd, calculate_rsi},
     volatility::calculate_bollinger_bands,

examples/macd_basic.rs

@@ -1,5 +1,5 @@
 use polars::prelude::*;
-use ta_lib_in_rust::indicators::oscillators::calculate_macd;
+use rusttalib::indicators::oscillators::calculate_macd;
 
 fn main() -> Result<(), PolarsError> {
     // Create a simple price series with some movement

examples/moving_averages_basic.rs

@@ -1,5 +1,5 @@
 use polars::prelude::*;
-use ta_lib_in_rust::indicators::moving_averages::{calculate_ema, calculate_sma, calculate_wma};
+use rusttalib::indicators::moving_averages::{calculate_ema, calculate_sma, calculate_wma};
 
 fn main() -> Result<(), PolarsError> {
     // Create a simple price series with trend followed by sideways movement

examples/rsi_basic.rs

@@ -1,5 +1,5 @@
 use polars::prelude::*;
-use ta_lib_in_rust::indicators::oscillators::calculate_rsi;
+use rusttalib::indicators::oscillators::calculate_rsi;
 
 fn main() -> Result<(), PolarsError> {
     // Create a simple price series with some movement

examples/stock/mean_reversion.rs

@@ -2,9 +2,9 @@
 // This example demonstrates a simple mean reversion strategy using technical indicators
 
 use polars::prelude::*;
-use ta_lib_in_rust::indicators::moving_averages::calculate_sma;
-use ta_lib_in_rust::indicators::oscillators::calculate_rsi;
-use ta_lib_in_rust::indicators::volatility::calculate_bollinger_bands;
+use rusttalib::indicators::moving_averages::calculate_sma;
+use rusttalib::indicators::oscillators::calculate_rsi;
+use rusttalib::indicators::volatility::calculate_bollinger_bands;
 
 fn main() -> Result<(), PolarsError> {
     println!("Mean Reversion Strategy Example");

examples/stock/trend_following.rs

@@ -2,9 +2,9 @@
 // This example demonstrates a simple trend following strategy using technical indicators
 
 use polars::prelude::*;
-use ta_lib_in_rust::indicators::moving_averages::calculate_sma;
-use ta_lib_in_rust::indicators::oscillators::{calculate_macd, calculate_rsi};
-use ta_lib_in_rust::indicators::trend::calculate_adx;
+use rusttalib::indicators::moving_averages::calculate_sma;
+use rusttalib::indicators::oscillators::{calculate_macd, calculate_rsi};
+use rusttalib::indicators::trend::calculate_adx;
 
 fn main() -> Result<(), PolarsError> {
     println!("Trend Following Strategy Example");

examples/vwap_basic.rs

@@ -2,7 +2,7 @@
 // This example demonstrates how to calculate and visualize VWAP
 
 use polars::prelude::*;
-use ta_lib_in_rust::indicators::moving_averages::calculate_vwap;
+use rusttalib::indicators::moving_averages::calculate_vwap;
 
 fn main() -> Result<(), PolarsError> {
     println!("VWAP (Volume Weighted Average Price) Example");

examples/working_with_multi_stock_data.rs

@@ -1,11 +1,11 @@
 use polars::prelude::*;
-use std::convert::TryInto;
-use ta_lib_in_rust::indicators::{
+use rusttalib::indicators::{
     moving_averages::{calculate_ema, calculate_sma},
     oscillators::{calculate_macd, calculate_rsi},
     volatility::{calculate_atr, calculate_bollinger_bands},
     volume::calculate_obv,
 };
+use std::convert::TryInto;
 
 /// This example demonstrates how to load and process data from multiple stock CSV files
 /// that may have different column formats, and perform cross-stock comparisons.

src/indicators/oscillators/dpo.rs

@@ -8,7 +8,7 @@ pub fn calculate_dpo(df: &DataFrame, close_col: &str, period: usize) -> PolarsRe
     let len = df.height();
     let mut dpo = vec![f64::NAN; len];
     let shift = period / 2 + 1;
-    
+
     for (i, _) in (0..len).enumerate() {
         if i + 1 >= period {
             let sma: f64 = close

src/indicators/volume/cmf.rs

@@ -100,7 +100,12 @@ pub fn calculate_cmf(df: &DataFrame, window: usize) -> PolarsResult<Series> {
 
         // Use an iterator-based approach as suggested by Clippy
         let window_start = i - (window - 1);
-        for (idx, money_flow_vol) in money_flow_volumes.iter().enumerate().skip(window_start).take(window) {
+        for (idx, money_flow_vol) in money_flow_volumes
+            .iter()
+            .enumerate()
+            .skip(window_start)
+            .take(window)
+        {
             let vol = volume.get(idx).unwrap_or(f64::NAN);
 
             if !money_flow_vol.is_nan() && !vol.is_nan() {