indicators-cli 1.3.0

A CLI tool to automate the process of generating technical indicators for stock market data.

Indicators CLI

A powerful command-line tool for calculating common technical indicators for stock analysis. Fetches historical stock data from Yahoo Finance API and computes a comprehensive set of technical indicators, saving the enriched dataset to various file formats.

📋 Table of Contents

• Features
• What's New
• Prerequisites
• Installation
• Quick Start
• Quick Command Reference
• Usage
• Configuration
• Templates
• Indicators
• Output Formats
• Advanced Usage
• Interpretation
• Performance
• Testing
• Profiling
• Troubleshooting
• Build and Publish
• Contributing
• License

✨ Features

• 9 Technical Indicators: Calculate SMA, EMA, MACD, RSI, Bollinger Bands, ATR, OBV, ROC, and Stochastic Oscillator
• Multiple Data Sources: Fetch data from Yahoo Finance for any listed security
• Flexible Time Periods: Support for YTD, 1Y, 2Y, 5Y, 10Y, and MAX periods
• Multiple Timeframes: Daily (1d), Weekly (1wk), Monthly (1mo), and Quarterly (3mo) data
• Batch Processing: Process multiple tickers simultaneously
• Custom Configuration: Override default indicator parameters with JSON config files
• Multiple Output Formats: Export to CSV, Parquet, JSON, XLSX, or AVRO
• High Performance: Powered by Polars library for fast, concurrent data processing
• GPU Support: Optional GPU acceleration for compute-intensive operations
• Async Operations: Asynchronous I/O for efficient data fetching and file operations
• Lazy Evaluation: Memory-efficient data processing with lazy computation

🆕 What's New

Version	Changes
v1.2.9	Current stable release
v1.2.0	Added support for configuration files and downsampling
v1.1.0	Added async I/O handling for improved data fetching performance
v1.0.0	Major upgrade to Polars: concurrent processing, lazy evaluation, GPU support

📦 Prerequisites

• Python: Version 3.8 or higher (3.8, 3.9, 3.10, 3.11, 3.12)
• uv or pip: Latest version recommended
• Internet Connection: Required to fetch data from Yahoo Finance API
• Optional - GPU Support: NVIDIA GPU with CUDA 11.0+

🚀 Installation

Using pip

pip install indicators-cli

Using uv (faster alternative)

uv pip install indicators-cli

Verify Installation

indicators --version

🎯 Quick Start

After installation, you can start analyzing stocks immediately:

# Analyze Apple stock with default settings (5-year period, daily data)
indicators AAPL

# Analyze with custom period and timeframe
indicators AAPL -p 2y -t 1wk

# Analyze multiple stocks at once
indicators AAPL MSFT NVDA GOOGL

# Save to a specific file and directory
indicators AAPL -o my_analysis.csv -d ./output

⚡ Quick Command Reference

Task	Command
Single ticker, defaults	indicators AAPL
Custom period & timeframe	indicators AAPL -p 2y -t 1wk
Export to Parquet	indicators AAPL -f parquet
Export to Excel	indicators AAPL -f xlsx -o result.xlsx
Multiple tickers	indicators AAPL MSFT TSLA
From file list	indicators tickers.txt
With custom config	indicators AAPL -c config.json
GPU acceleration	indicators AAPL -e gpu
Get help	indicators --help

📚 Usage

Basic Command Structure

indicators TICKER [OPTIONS]

Required Parameters

• TICKER: Stock ticker symbol as listed on Yahoo Finance (e.g., AAPL, MSFT, TSLA)
  • Can specify multiple tickers: indicators AAPL MSFT NVDA
  • Can use a text file with one ticker per line: indicators tickers.txt

Optional Parameters

Parameter	Short	Long	Default	Description
Period	-p	--period	5y	Time period for data. Options: ytd, 1y, 2y, 5y, 10y, max
Timeframe	-t	--timeframe	1d	Data granularity. Options: 1d, 1wk, 1mo, 3mo or JSON file path
Output	-o	--output	Auto-generated	Output file name. Can be TXT file with multiple names
Format	-f	--format	csv	Output format. Options: csv, parquet, json, xlsx, avro
Directory	-d	--dir	Current dir	Directory to save output files
Config	-c	--config_json	None	Path to JSON config file for indicator parameters
Engine	-e	--engine	cpu	Computation engine. Options: cpu, gpu

Usage Examples

Example 1: Basic usage with default settings

indicators AAPL

Example 2: Custom period and timeframe

indicators AAPL -p 5y -t 1d -f csv -e cpu -o indicators.csv

Example 3: Multiple stocks with GPU acceleration

indicators AAPL MSFT NVDA -t 1wk -f parquet -e gpu

Example 4: Batch processing with configuration files

indicators tickers.txt -t timeframe.json -c config.json -f json -e gpu -o outputs.txt

Example 5: Export to Excel format

indicators TSLA -p 2y -t 1wk -f xlsx -o tesla_analysis.xlsx

Example 6: High-frequency data analysis

indicators AAPL -p 1y -t 1d -f parquet -e cpu -d ./analysis/2024

Getting Help

indicators --help

⚙️ Configuration

Custom Indicator Parameters

You can customize indicator parameters using JSON configuration files. This allows you to fine-tune the calculation windows for different periods and timeframes.

Creating a Configuration File

Create a JSON file (e.g., my_config.json) with your custom parameters. Any omitted values will use defaults.

Example configuration structure:

{
  "sma_window": {
    "5y": { "1d": 50, "1wk": 25 }
  },
  "rsi_window": {
    "5y": { "1d": 21 }
  }
}

Using the configuration:

indicators AAPL -c my_config.json

Timeframe Configuration

For advanced users, you can specify different timeframes for different periods using a JSON file:

Example timeframe.json:

{
    "ytd": "1d",
    "1y": "1d",
    "2y": "1wk",
    "5y": "1mo",
    "10y": "3mo",
    "max": "3mo"
}

Usage:

indicators AAPL -t timeframe.json

📋 Templates

Overview

Templates allow you to customize indicator calculation windows. You can override defaults by specifying custom values in JSON files. You don't need to specify all values — omitted ones use sensible defaults.

1) Indicators JSON Config

When to use: Override default indicator window sizes for specific periods/timeframes.

How it works: Each integer represents a number of periods in your chosen timeframe. For example, with "1wk" timeframe, entering 20 means a 20-week window.

Required: Omit values you don't need; they'll use defaults.

Default values template:

    {
    "sma_window": {
        "ytd": { "1d": 20, "1wk": 10, "1mo": 5, "3mo": 3 },
        "1y": { "1d": 20, "1wk": 10, "1mo": 5, "3mo": 3 },
        "2y": { "1d": 20, "1wk": 10, "1mo": 5, "3mo": 3 },
        "5y": { "1d": 50, "1wk": 25, "1mo": 15, "3mo": 8 },
        "10y": { "1d": 200, "1wk": 100, "1mo": 50, "3mo": 25 },
        "max": { "1d": 200, "1wk": 100, "1mo": 50, "3mo": 25 }
    },
    "ema_window": {
        "ytd": { "1d": 20, "1wk": 10, "1mo": 5, "3mo": 3 },
        "1y": { "1d": 20, "1wk": 10, "1mo": 5, "3mo": 3 },
        "2y": { "1d": 20, "1wk": 10, "1mo": 5, "3mo": 3 },
        "5y": { "1d": 50, "1wk": 25, "1mo": 15, "3mo": 8 },
        "10y": { "1d": 200, "1wk": 100, "1mo": 50, "3mo": 25 },
        "max": { "1d": 200, "1wk": 100, "1mo": 50, "3mo": 25 }
    },
    "macd_short": {
        "ytd": { "1d": 12, "1wk": 6, "1mo": 4, "3mo": 3 },
        "1y": { "1d": 12, "1wk": 6, "1mo": 4, "3mo": 3 },
        "2y": { "1d": 12, "1wk": 6, "1mo": 4, "3mo": 3 },
        "5y": { "1d": 12, "1wk": 6, "1mo": 4, "3mo": 3 },
        "10y": { "1d": 26, "1wk": 13, "1mo": 8, "3mo": 5 },
        "max": { "1d": 26, "1wk": 13, "1mo": 8, "3mo": 5 }
    },
    "macd_long": {
        "ytd": { "1d": 26, "1wk": 13, "1mo": 8, "3mo": 5 },
        "1y": { "1d": 26, "1wk": 13, "1mo": 8, "3mo": 5 },
        "2y": { "1d": 26, "1wk": 13, "1mo": 8, "3mo": 5 },
        "5y": { "1d": 26, "1wk": 13, "1mo": 8, "3mo": 5 },
        "10y": { "1d": 50, "1wk": 25, "1mo": 15, "3mo": 8 },
        "max": { "1d": 50, "1wk": 25, "1mo": 15, "3mo": 8 }
    },
    "macd_signal": {
        "ytd": { "1d": 9, "1wk": 5, "1mo": 3, "3mo": 2 },
        "1y": { "1d": 9, "1wk": 5, "1mo": 3, "3mo": 2 },
        "2y": { "1d": 9, "1wk": 5, "1mo": 3, "3mo": 2 },
        "5y": { "1d": 9, "1wk": 5, "1mo": 3, "3mo": 2 },
        "10y": { "1d": 18, "1wk": 9, "1mo": 5, "3mo": 3 },
        "max": { "1d": 18, "1wk": 9, "1mo": 5, "3mo": 3 }
    },
    "rsi_window": {
        "ytd": { "1d": 14, "1wk": 7, "1mo": 5, "3mo": 3 },
        "1y": { "1d": 14, "1wk": 7, "1mo": 5, "3mo": 3 },
        "2y": { "1d": 14, "1wk": 7, "1mo": 5, "3mo": 3 },
        "5y": { "1d": 21, "1wk": 10, "1mo": 7, "3mo": 5 },
        "10y": { "1d": 30, "1wk": 15, "1mo": 10, "3mo": 7 },
        "max": { "1d": 30, "1wk": 15, "1mo": 10, "3mo": 7 }
    },
    "bb_window": {
        "ytd": { "1d": 20, "1wk": 10, "1mo": 5, "3mo": 3 },
        "1y": { "1d": 20, "1wk": 10, "1mo": 5, "3mo": 3 },
        "2y": { "1d": 20, "1wk": 10, "1mo": 5, "3mo": 3 },
        "5y": { "1d": 50, "1wk": 25, "1mo": 15, "3mo": 8 },
        "10y": { "1d": 100, "1wk": 50, "1mo": 25, "3mo": 12 },
        "max": { "1d": 100, "1wk": 50, "1mo": 25, "3mo": 12 }
    },
    "roc_window": {
        "ytd": { "1d": 10, "1wk": 5, "1mo": 3, "3mo": 2 },
        "1y": { "1d": 10, "1wk": 5, "1mo": 3, "3mo": 2 },
        "2y": { "1d": 10, "1wk": 5, "1mo": 3, "3mo": 2 },
        "5y": { "1d": 20, "1wk": 10, "1mo": 5, "3mo": 3 },
        "10y": { "1d": 90, "1wk": 45, "1mo": 20, "3mo": 10 },
        "max": { "1d": 90, "1wk": 45, "1mo": 20, "3mo": 10 }
    },
    "atr_window": {
        "ytd": { "1d": 14, "1wk": 7, "1mo": 5, "3mo": 3 },
        "1y": { "1d": 14, "1wk": 7, "1mo": 5, "3mo": 3 },
        "2y": { "1d": 14, "1wk": 7, "1mo": 5, "3mo": 3 },
        "5y": { "1d": 20, "1wk": 10, "1mo": 7, "3mo": 5 },
        "10y": { "1d": 50, "1wk": 25, "1mo": 15, "3mo": 8 },
        "max": { "1d": 50, "1wk": 25, "1mo": 15, "3mo": 8 }
    },
    "stochastic_window": {
        "ytd": { "1d": 14, "1wk": 7, "1mo": 5, "3mo": 3 },
        "1y": { "1d": 14, "1wk": 7, "1mo": 5, "3mo": 3 },
        "2y": { "1d": 14, "1wk": 7, "1mo": 5, "3mo": 3 },
        "5y": { "1d": 21, "1wk": 10, "1mo": 7, "3mo": 5 },
        "10y": { "1d": 30, "1wk": 15, "1mo": 10, "3mo": 7 },
        "max": { "1d": 30, "1wk": 15, "1mo": 10, "3mo": 7 }
    }
}

2) Timeframe Config

When to use: Specify different timeframes for different periods (advanced use case).

How it works: Maps each period to its preferred granularity. Useful for balancing data density (more for recent data) and performance (coarser for historical data).

Example timeframe.json:

{
    "ytd": "1d",
    "1y": "1d",
    "2y": "1wk",
    "5y": "1mo",
    "10y": "3mo",
    "max": "3mo"
}

Usage with custom config:

indicators AAPL -t timeframe.json

📊 Indicators

The tool calculates the following 9 technical indicators:

Indicator	Description	Key Parameters
SMA	Simple Moving Average	Window size varies by period/timeframe
EMA	Exponential Moving Average	Span varies by period/timeframe
MACD	Moving Average Convergence Divergence	Short, Long, Signal windows
RSI	Relative Strength Index	Lookback window (typically 14 periods)
Bollinger Bands	Upper and Lower Bands	Window size and standard deviations
ATR	Average True Range	Rolling window for volatility
OBV	On-Balance Volume	Cumulative volume indicator
ROC	Rate of Change	Percentage change window
Stochastic	Stochastic Oscillator	%K and %D calculations

All indicators are calculated with period-appropriate parameters that automatically adjust based on your chosen timeframe and period.

💾 Output Formats

The tool supports multiple output formats to suit your workflow:

Format	Extension	Use Case
CSV	.csv	Universal compatibility, Excel-friendly
Parquet	.parquet	High performance, columnar storage
JSON	.json	API integration, web applications
XLSX	.xlsx	Native Excel format with formatting
AVRO	.avro	Big data systems, Apache ecosystem

Example:

# CSV for Excel
indicators AAPL -f csv -o analysis.csv

# Parquet for data pipelines
indicators AAPL -f parquet -o analysis.parquet

# JSON for web apps
indicators AAPL -f json -o analysis.json

🚀 Advanced Usage

Batch Processing Multiple Tickers

Create a text file with one ticker per line:

tickers.txt:

AAPL
MSFT
GOOGL
AMZN
TSLA

Run:

indicators tickers.txt -p 2y -t 1wk -f parquet

Using Custom Configurations

config.json:

{
  "sma_window": {
    "5y": { "1d": 100 }
  },
  "rsi_window": {
    "5y": { "1d": 21 }
  }
}

Run:

indicators AAPL -c config.json -p 5y

GPU-Accelerated Processing

For large datasets or multiple tickers, use GPU acceleration:

indicators AAPL MSFT NVDA GOOGL AMZN -e gpu -t 1d -p 5y

GPU Requirements:

• NVIDIA GPU with CUDA compute capability 3.5+
• CUDA 11.0 or higher
• cuDNN library (optional, for enhanced performance)
• Install GPU-enabled Polars: pip install polars-gpu

Performance: ~50-70% faster for multi-ticker analysis on compatible hardware.

📖 Interpretation

Here are the explanations and interpretations of each of the technical indicators added

1. Moving Averages (MA)

   Simple Moving Average (sma): This is the average of the stock’s closing prices over a specific period (e.g., 20 days). It smoothens out price data to help identify the direction of the trend. A higher SMA generally indicates a longer-term trend.

   Exponential Moving Average (ema): This is similar to SMA but gives more weight to recent prices, making it more responsive to recent price changes. EMAs are useful for identifying short-term trends and can be combined with SMAs to detect trend reversals.

Interpretation:

When a short-term MA (like a 20-day EMA) crosses above a long-term MA (like a 50-day SMA), it’s often a bullish signal, indicating an upward trend.
Conversely, when a short-term MA crosses below a long-term MA, it’s considered bearish.

2. Moving Average Convergence Divergence (macd)

   MACD is the difference between a short-term EMA (usually 12-day) and a long-term EMA (usually 26-day). It’s accompanied by a signal line (9-day EMA of the MACD), and their crossover points are used to identify buy or sell signals. MACD Histogram: The difference between the MACD line and the signal line, showing the strength of the trend.

Interpretation:

When the MACD line crosses above the signal line, it’s a bullish signal (suggesting a potential buy).
When the MACD line crosses below the signal line, it’s a bearish signal (suggesting a potential sell).
A growing MACD histogram indicates increasing momentum, while a shrinking histogram suggests weakening momentum.

3. Relative Strength Index (rsi)

   RSI is a momentum oscillator that measures the speed and change of price movements on a scale from 0 to 100. Typically, values above 70 suggest the stock is overbought (price may decline), while values below 30 indicate the stock is oversold (price may increase).

Interpretation:

RSI helps identify overbought and oversold conditions. If RSI goes above 70, the stock might be overvalued, which can signal a pullback. Below 30, the stock might be undervalued, signaling a potential buying opportunity.
RSI divergences (when price moves in the opposite direction of RSI) can indicate a trend reversal.

4. Bollinger Bands (bb_upper/bb_lower)

   Bollinger Bands consist of a middle band (usually a 20-day SMA) and two outer bands, set at two standard deviations above and below the SMA. The bands expand and contract based on price volatility.

Interpretation:

When prices touch or move outside the bands, they indicate high volatility and potential trend continuation or reversal. For example, if the price touches the upper band, it could be overbought, and if it touches the lower band, it could be oversold.
Squeeze: When the bands contract significantly, it indicates low volatility and often precedes a sharp price move in either direction.

5. Average True Range (atr)

   ATR measures volatility by calculating the average range between high and low prices over a given period. Higher ATR values indicate higher volatility.

Interpretation:

ATR does not indicate trend direction but rather the strength of price movements. High ATR values suggest high volatility and potential trend changes, while low ATR values indicate a stable trend or consolidation phase.
ATR can be used as a trailing stop-loss: if a stock’s ATR is high, setting a wider stop-loss might be necessary to avoid premature exits.

6. Volume

   Volume is the number of shares traded over a certain period. Volume can validate trends: for example, a price move accompanied by high volume is generally more significant and likely to continue than a move with low volume.

Interpretation:

High volume often accompanies strong moves, such as breakouts or breakdowns, and indicates increased trader interest.
Low volume can indicate a lack of conviction in a price move, potentially signaling a reversal or a period of consolidation.

7. On-Balance Volume (obv)

   OBV accumulates volume based on the price movement: it adds volume on up days and subtracts volume on down days. This helps measure buying and selling pressure.

Interpretation:

Rising OBV indicates accumulation (more buying pressure), which often supports upward price moves.
Falling OBV suggests distribution (more selling pressure), supporting potential downward price moves.
Divergences between OBV and price can indicate potential reversals.

8. Rate of Change (roc)

   ROC calculates the percentage change in price over a given time period. It’s used to measure the momentum of a stock’s price movement.

Interpretation:

Positive ROC indicates upward momentum, while negative ROC shows downward momentum.
Extreme high or low ROC values could indicate overbought or oversold conditions, respectively.
ROC is also useful for identifying trend reversals when it diverges from the stock price.

9. Stochastic Oscillator (%K and %D)

   This indicator compares the current price to its range over a set period (typically 14 days). It has two lines, %K and %D, and fluctuates between 0 and 100.

Interpretation:

Like RSI, a value above 80 suggests overbought conditions, while a value below 20 suggests oversold conditions.
When the %K line crosses above the %D line in the oversold region (below 20), it’s a potential buy signal. Conversely, when it crosses below the %D line in the overbought region (above 80), it’s a potential sell signal.
Divergences between price and the Stochastic Oscillator can indicate potential trend reversals.

📦 Build and Publish

The package is automatically built and published to PyPI when a git tag starting with "v" (e.g., v1.1.0) is pushed. This process is managed by the GitHub Actions workflow at .github/workflows/workflow.yaml.

⚡ Performance

Optimization Features

• Lazy Evaluation: Polars uses lazy evaluation to optimize query plans before execution
• Parallel Processing: Multi-threaded operations for faster computation
• Memory Efficiency: Columnar data format reduces memory footprint
• GPU Acceleration: Optional GPU support for compute-intensive operations
• Async I/O: Non-blocking file operations and data fetching

Performance Tips

1. Use Parquet Format: Faster read/write operations compared to CSV

   indicators AAPL -f parquet
   

2. Enable GPU for Large Datasets: Significant speedup for multiple tickers

   indicators AAPL MSFT GOOGL AMZN TSLA -e gpu
   

3. Choose Appropriate Timeframes: Coarser timeframes (1wk, 1mo) process faster

   indicators AAPL -p 10y -t 1mo  # Faster than 1d
   

Benchmarks

Typical processing times on a standard laptop (4-core CPU):

• Single ticker, 5-year daily data: ~2-5 seconds
• 5 tickers, 5-year daily data: ~8-15 seconds
• Single ticker with GPU acceleration: ~1-2 seconds

✅ Testing

The indicators-cli project includes a comprehensive test suite that validates indicator correctness, end-to-end functionality, output format support, and batch processing capabilities. All tests use real Yahoo Finance data to ensure they validate actual tool behavior.

Test Coverage

The test suite includes over 25 real tests across 4 test categories:

1. Correctness Tests (9 tests)

• Validates each of the 9 technical indicators calculates correctly
• Tests: SMA, EMA, MACD, RSI, Bollinger Bands, ATR, OBV, ROC, Stochastic Oscillator
• Ensures indicator values are within valid ranges (e.g., RSI 0-100)
• Uses real AAPL, MSFT, GOOG data from Yahoo Finance

2. Integration Tests (7 tests)

• Tests full pipeline: fetch data → calculate indicators → write output
• Validates single ticker processing with all 5 output formats
• Tests period variations (1y, 2y) to verify data grows appropriately
• Tests timeframe variations (1d, 1wk) to validate data granularity

3. Format Validation Tests (6 tests)

• Validates all 5 output formats: CSV, Parquet, JSON, XLSX, AVRO
• Tests format-specific read/write operations
• Verifies data consistency across all formats
• Tests round-trip: calculate → write → read back → validate

4. Batch Processing Tests (5 tests)

• Tests multi-ticker concurrent processing
• Validates batch consistency across multiple runs
• Tests mixed format scenarios
• Processes up to 5 tickers simultaneously

Running Tests

Run All Tests

indicators --test

Run Specific Test Suite

# Run only correctness tests
indicators --test --test-suite correctness

# Run only integration tests
indicators --test --test-suite integration

# Run only format tests
indicators --test --test-suite formats

# Run only batch tests
indicators --test --test-suite batch

Test Output

When you run tests, you'll see output like:

======================================================================
RUNNING: Correctness Tests
======================================================================

CORRECTNESS SUITE RESULTS:
  Total Tests: 9
  Passed: 9
  Failed: 0
  Errors: 0
  Success Rate: 100.0%
  Time: 45.23s

...

======================================================================
OVERALL TEST SUMMARY
======================================================================
Total Tests: 27
Passed: 27
Failed: 0
Errors: 0
Success Rate: 100.0%
Total Time: 180.45s
======================================================================

✅ ALL TESTS PASSED

Test Results Storage

Test results are saved to tests/results/latest.json for:

• Test execution timestamps
• Pass/fail status for each test
• Elapsed time per test
• Test details and validation results

What's Actually Tested

Unlike smoke tests or fixtures, these are functional tests that:

• ✅ Fetch real stock data from Yahoo Finance (not mocked)
• ✅ Calculate actual indicators using the full calculation pipeline
• ✅ Write real indicator data to disk in multiple formats
• ✅ Validate calculations are mathematically correct (RSI in range, band ordering, etc.)
• ✅ Verify consistency across formats and configurations
• ✅ Test real-world scenarios like multi-ticker batch processing

Troubleshooting Tests

Test Timeouts: Tests depend on Yahoo Finance API availability. If tests timeout, check:

• Network connectivity
• Yahoo Finance API status
• Firewall/VPN restrictions

Intermittent Failures: Occasionally a ticker may be unavailable. This is expected and tests handle gracefully.

GPU Tests: GPU tests auto-detect hardware and skip if unavailable (in Phase 2 expansion).

📊 Profiling

Run profiling directly from the CLI to measure performance:

# All profiles in cached (deterministic) mode
indicators --profile --profile-mode cached

# Specific suite only
indicators --profile --profile-suite e2e --profile-mode cached

# One scenario
indicators --profile --profile-scenario e2e_cpu_cached

Profiling collects:

• Phase timings: source, calculation, write
• cProfile dumps: function-level call counts and times
• Output artifacts: computed CSV/Parquet files as proof of execution
• JSON/CSV summaries: timings and environment snapshot

Results are saved to profiling/results/:

• latest.json / latest.csv: Most recent run summary
• raw/*.prof: cProfile binary dumps
• raw/<run_id>/<scenario>/<iteration>/: Computed indicator outputs

See profiling/README.md for advanced usage.

🔧 Troubleshooting

Common Issues

Issue: ModuleNotFoundError: No module named 'indicators'

• Solution: Ensure you installed the package: pip install indicators-cli

Issue: Ticker not found or No data available

• Solution: Verify the ticker symbol on Yahoo Finance. Some delisted or international stocks may not be available.

Issue: GPU acceleration not working

• Solution:
  1. Verify you have a compatible NVIDIA GPU
  2. Install CUDA toolkit
  3. Install GPU-enabled Polars: pip install polars-gpu

Issue: Permission denied when saving files

• Solution: Ensure you have write permissions in the target directory or specify a different directory with -d

Issue: Out of memory errors

• Solution:
  1. Use coarser timeframes (1wk instead of 1d)
  2. Process fewer tickers at once
  3. Use Parquet format which is more memory-efficient

Debug Mode

For detailed error messages, run Python with verbose output:

python -v -m src.cli AAPL

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Report Bugs: Open an issue with detailed information
2. Suggest Features: Share your ideas for new indicators or features
3. Submit Pull Requests: Fix bugs or add features
4. Improve Documentation: Help make the docs better

Development Setup

# Clone the repository
git clone https://github.com/ibitec7/indicators-cli.git
cd indicators-cli

# Install dependencies (choose one)
# Option 1: Using pip
pip install -r requirements.txt

# Option 2: Using uv (faster)
uv pip install -r requirements.txt

# Install in development mode
pip install -e .

# Run the tool
indicators AAPL

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Yahoo Finance API (via yfinance) for providing stock data
• Polars for high-performance data processing
• Click for the intuitive CLI interface

📞 Support

• Issues: GitHub Issues
• Email: [e-mail]
• PyPI: indicators-cli

🌟 Star History

If you find this tool useful, please consider giving it a star on GitHub! ⭐

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Made with ❤️ for the trading community