Quant-Trading

Python backtesting and parameter-optimization framework for 5 trading strategies, each paired with a TradingView Pine Script. Scan the S&P 500 daily for live signals, backtest any strategy on any ticker, and visualize results with one command.

Featured Strategies

Strategy	Type	Win Rate	Avg Hold
Elektro BB	Mean reversion (BB + RSI oversold entry)	~80-100%	Months-years
DCA Long	Always-in with safety order ladder	~100%	Weeks-months
Bollinger WMA	Breakout above WMA	~87%	Months-years
RMA ATR Bands	Asymmetric ATR channel trend	~64%	Weeks-months
EMA Trail	EMA crossover + ratcheting trailing stop	~50-60%	Weeks

All strategies are long-only, optimized across 10 large-cap US stocks, backtested 2021-2026 on daily bars, $1,500 capital, 33% risk per trade, 1.5% commission per leg.

Install

conda env create -f requirements_conda.yml
conda activate trading

Usage

Visualize a backtest - price chart, equity curve, drawdown:

python visualize.py --strategy elektro --ticker NVDA
python visualize.py --strategy rma_atr --ticker AAPL --save

Strategies: rma_atr · ema_trail · bb_wma · elektro · dca

Compare all 5 strategies on one ticker:

python compare.py --ticker NVDA

Run the daily scanner (after market close):

python featured/strategy_elektro/scanner.py
python featured/strategy_dca/scanner.py
python featured/strategy_rma_atr/scanner.py
python featured/strategy_bb_wma/scanner.py
python featured/strategy_ema_trail/scanner.py

Re-optimize parameters (cross-asset grid search):

python featured/strategy_rma_atr/tests/test_largecap.py

TradingView Pine Scripts - ready to paste, defaults set to optimized params:

featured/strategy_<name>/pine_<name>_largecap.pine

Credits

See CREDITS.md for the original TradingView Pine Script authors this repo is based on.

Uses yfinance - free for personal/educational use only.