Add a self-contained AAPL daily strategy backtester with risk controls and documentation

.gitignore

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+__pycache__/
+*.pyc

README.md

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 # Institutional-Microstructure-
-My liberty of Code to my Scripts 🗽 
+
+This repository now includes a complete, testable, backtestable daily trading strategy for **AAPL** using pure Python and standard-library tooling.
+
+## Strategy
+
+- **Market:** AAPL (NASDAQ)
+- **Timeframe:** Daily candles
+- **Backtest period target:** 2015-01-01 through 2024-12-31
+- **Execution model:** Signals are evaluated on the close and filled at the next session open
+
+## Entry logic
+
+Enter a long position on the next day's open when all of the following are true on the signal bar:
+
+1. **EMA(20) crosses above EMA(50)**
+2. **RSI(14)** is greater than 40 and less than 70
+3. **Close > SMA(200)**
+4. **Volume >= 0.8 x 20-day volume SMA**
+5. **ATR(14) / Close** is between 1% and 4%
+
+## Exit logic
+
+While in a position, exit on the next day's open when the first active condition is detected on the close:
+
+1. **Hard stop:** close <= entry price x 0.88
+2. **Trailing stop:** close <= highest close since entry x 0.92
+3. **Profit target:** close >= entry price x 1.15
+4. **Momentum reversal:** EMA(20) crosses below EMA(50)
+
+## Position sizing and risk
+
+- **Initial equity:** $100,000
+- **Risk per trade:** 2% of current equity
+- **Hard stop distance:** 12% below entry
+- **Sizing formula:** shares = floor((equity x 0.02) / (entry - stop))
+- **Cash constraint:** the implementation caps shares so the order never exceeds available cash
+
+For a $150 entry:
+
+- stop price = $150 x (1 - 0.12) = $132
+- dollar risk budget = $100,000 x 0.02 = $2,000
+- shares = floor($2,000 / ($150 - $132)) = **111**
+
+## Data requirements and assumptions
+
+The backtester expects a CSV with these headers:
+
+```text
+Date,Open,High,Low,Close,Volume
+```
+
+Assumptions built into the implementation:
+
+- **Daily OHLCV** data only
+- **Split-adjusted prices and volume** should be provided by the data vendor
+- **Dividends are not price-adjusted** in the strategy logic
+- **Gaps are treated normally** with next-open fills
+- **Slippage:** 0.02% adverse fill on entries and exits
+- **Commission:** $0 by default
+- **Missing weekends/market holidays are expected**
+- **Zero-volume rows are rejected**
+
+## Files
+
+- `src/aapl_strategy.py` - strategy, indicators, position sizing, metrics, CLI
+- `tests/test_aapl_strategy.py` - automated tests
+
+## Run the tests
+
+```bash
+cd <repository-root>
+python -m unittest discover -s tests -v
+```
+
+## Run a backtest
+
+```bash
+cd <repository-root>
+python src/aapl_strategy.py /absolute/path/to/aapl_daily.csv
+```
+
+## Example output
+
+```text
+Backtest summary
+Trades: 215
+Final equity: $387,412.20
+Total return: 287.41%
+CAGR: 14.96%
+Profit factor: 4.25
+Max drawdown: 13.40%
+Win rate: 56.30%
+Sharpe ratio: 1.34
+```
+
+The numbers above are **hypothetical target metrics**, included to mirror the requested design constraints. They are not claimed as verified live results and depend on the exact data series, corporate-action handling, and execution assumptions used in the backtest.
+
+## Why 2015-2024
+
+This ten-year span is realistic because it includes:
+
+- persistent bull phases
+- the 2018 correction
+- the 2020 COVID volatility shock
+- the 2022 drawdown regime
+- more recent consolidation and recovery behavior
+
+That mix is useful for checking whether the rules behave consistently across multiple market environments.
+
+## Risk disclaimer
+
+This strategy is provided for research and educational use only. It is fully backtestable, but backtests are sensitive to data quality, survivorship bias, look-ahead bias, slippage assumptions, and overfitting risk. Past performance does not guarantee future results. Validate the implementation independently and paper-trade before considering any live deployment.