semvec/pypi-packages huggingface dataset:
HowdenPipeline 3.1.0
A simple configuration manager with Pydantic and JSON export.
License: MIT
Requires Python: <3.14,>=3.12
Keywords: config, configuration, pydantic, json

# Introduction 
TODO: Give a short introduction of your project. Let this section explain the objectives or the motivation behind this project. 

# Getting Started
TODO: Guide users through getting your code up and running on their own system. In this section you can talk about:
1.	Installation process
2.	Software dependencies
3.	Latest releases
4.	API references

# Build and Test
TODO: Describe and show how to build your code and run the tests. 

# Contribute
TODO: Explain how other users and developers can contribute to make your code better. 

If you want to learn more about creating good readme files then refer the following [guidelines](https://docs.microsoft.com/en-us/azure/devops/repos/git/create-a-readme?view=azure-devops). You can also seek inspiration from the below readme files:
- [ASP.NET Core](https://github.com/aspnet/Home)
- [Visual Studio Code](https://github.com/Microsoft/vscode)
- [Chakra Core](https://github.com/Microsoft/ChakraCore)
bigtest 2.0.1
Holy Grail Backtester - Institutional-grade quant backtesting engine with 100x leverage support
License: MIT
Requires Python: >=3.8
Keywords: backtesting, trading, quantitative-finance, algorithmic-trading, forex, crypto, HFT

# BigTest - Holy Grail Backtester v2.0

**Institutional-grade backtesting engine** for quantitative trading research. Supports 100x leverage, HFT strategies, and zero-bias execution.

## Features

- 🎯 **Zero-Bias i+1 Execution** - Signals execute on next bar's open, eliminating look-ahead bias
- 💰 **100x Leverage Support** - Real-time margin monitoring with automatic liquidation
- 📊 **Raw Bid/Ask Microstructure** - True market reality using Dukascopy tick data
- 📋 **Limit Order System** - Proper fill logic with maker/taker commission
- 📈 **ATR-Based Slippage** - Variable slippage that scales with volatility
- ⚡ **Batch Tick Fetching** - Pre-load hours of tick data for performance

## Installation

```bash
pip install bigtest
```

With optional providers:
```bash
pip install bigtest[all]  # Alpaca + Binance
```

## Quick Start

```python
from bigtest import BacktestEngine, BacktestConfig

config = BacktestConfig(
    symbol='EUR/USD',
    timeframe='1m',
    start_date='2024-01-01',
    end_date='2024-02-01',
    initial_capital=10000,
    leverage=100,
    stop_out_enabled=True
)

def my_strategy(df):
    signals = []
    # Your strategy logic here
    return signals

engine = BacktestEngine(config)
metrics = engine.run(my_strategy)
engine.generate_html_report('my_report.html')
```

## Key Config Options

| Option | Default | Description |
|--------|---------|-------------|
| `leverage` | 1 | Leverage multiplier (up to 100x) |
| `stop_out_enabled` | True | Enable margin call liquidation |
| `use_notional_commission` | True | Commission on notional value |
| `slippage_model` | 'fixed' | 'none', 'fixed', 'random', 'volume', 'atr' |
| `maintenance_margin_pct` | 0.5 | Stop-out at 50% margin |

## Supported Data Sources

- **Forex**: Dukascopy (tick-level Bid/Ask)
- **Stocks**: Alpaca (requires API key)
- **Crypto**: Binance (BTC, ETH)

## License

MIT
pythonpine 2.0.1
High-performance vectorized Pine Script indicators for ML and backtesting
License: MIT
Requires Python: >=3.8
Keywords: technical-analysis, trading, indicators, pine-script, machine-learning, quantitative-finance, RSI, MACD, ATR

# PythonPine v2.0 - High-Performance Technical Indicators

**Vectorized Pine Script indicators** for machine learning and algorithmic trading. 10-100x faster than loop-based implementations.

## Features

- ⚡ **NumPy/Pandas Vectorization** - Millions of bars in seconds
- 🧠 **ML Alpha Module** - Z-score, percentile rank, fractional differentiation
- 📊 **50+ Indicators** - RSI, MACD, ATR, Bollinger, SuperTrend, Ichimoku...
- 🔄 **Pine Script Parity** - Same signatures, same math
- 📈 **Price Action** - Vectorized pattern detection

## Installation

```bash
pip install pythonpine
```

## Quick Start

```python
import numpy as np
from pythonpine import rsi, macd, atr, zscore

# Your price data
close = np.random.randn(10000).cumsum() + 100
high = close + np.abs(np.random.randn(10000)) * 0.5
low = close - np.abs(np.random.randn(10000)) * 0.5

# Calculate indicators (returns NumPy arrays)
rsi_values = rsi(close, 14)
macd_line, signal, hist = macd(close)
atr_values = atr(high, low, close, 14)

# ML preprocessing
rsi_zscore = zscore(rsi_values, 20)  # Normalized for ML
```

## ML Alpha Module

```python
from pythonpine import zscore, percentile_rank, log_returns, fractional_diff

# Stationarity transforms
returns = log_returns(close)
frac_diff = fractional_diff(close, d=0.4)  # López de Prado method

# Feature engineering
rsi_z = zscore(rsi(close), 50)  # Z-score normalized RSI
rsi_rank = percentile_rank(rsi(close), 100)  # Percentile rank
```

## Benchmarks

| Indicator | Legacy (loop) | V2 (vectorized) | Speedup |
|-----------|---------------|-----------------|---------|
| RSI | 1.2s | 0.01s | **120x** |
| MACD | 0.8s | 0.008s | **100x** |
| ATR | 0.5s | 0.005s | **100x** |
| Bollinger | 0.9s | 0.007s | **130x** |

*Tested on 1M bars*

## Indicator Reference

### Momentum
- `rsi(close, 14)` - Relative Strength Index
- `macd(close, 12, 26, 9)` - MACD line, signal, histogram
- `stochastic(close, high, low)` - Stochastic Oscillator
- `adx(high, low, close)` - Average Directional Index
- `cci(close, high, low)` - Commodity Channel Index

### Volatility
- `atr(high, low, close, 14)` - Average True Range
- `bollinger_bands(close, 20, 2)` - Upper, lower, middle
- `keltner_channel(high, low, close)` - Keltner bands
- `historical_volatility(close, 20)` - HV annualized

### Trend
- `sma(close, 20)` - Simple Moving Average
- `ema(close, 20)` - Exponential MA
- `supertrend(high, low, close)` - Trend direction + line
- `ichimoku(high, low, close)` - Full cloud

### Price Action
- `engulfing(o, h, l, c)` - Returns +1 (bullish), -1 (bearish), 0
- `doji(o, h, l, c)` - Doji detection
- `support_resistance_zones(h, l, c, v)` - Volume-based S/R

## License

MIT