Add trading script tests and CI workflow

.github/workflows/tests.yml

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+name: Tests
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: '3.x'
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pandas numpy yfinance pytest
+      - name: Run tests
+        run: pytest

tests/test_trading_script.py

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+import pandas as pd
+from types import SimpleNamespace
+import trading_script
+
+
+def test_set_data_dir_updates_paths(tmp_path):
+    new_dir = tmp_path / "data"
+    trading_script.set_data_dir(new_dir)
+    assert trading_script.DATA_DIR == new_dir
+    assert trading_script.PORTFOLIO_CSV == new_dir / "chatgpt_portfolio_update.csv"
+    assert trading_script.TRADE_LOG_CSV == new_dir / "chatgpt_trade_log.csv"
+    assert new_dir.exists()
+
+
+def test_process_portfolio_input_normalization(monkeypatch, tmp_path):
+    trading_script.set_data_dir(tmp_path)
+    trading_script.day = 0
+    monkeypatch.setattr("builtins.input", lambda *args, **kwargs: "")
+
+    class DummyTicker:
+        def __init__(self, ticker):
+            pass
+
+        def history(self, period):
+            return pd.DataFrame({"Low": [10.0], "Close": [10.0]})
+
+    monkeypatch.setattr(trading_script.yf, "Ticker", DummyTicker)
+
+    portfolio_variants = [
+        {
+            "ticker": ["ABC"],
+            "shares": [1],
+            "stop_loss": [4.0],
+            "buy_price": [5.0],
+        },
+        [
+            {
+                "ticker": "ABC",
+                "shares": 1,
+                "stop_loss": 4.0,
+                "buy_price": 5.0,
+            }
+        ],
+    ]
+
+    expected = pd.DataFrame(
+        {
+            "ticker": ["ABC"],
+            "shares": [1],
+            "stop_loss": [4.0],
+            "buy_price": [5.0],
+        }
+    )
+    for portfolio in portfolio_variants:
+        portfolio_df, cash = trading_script.process_portfolio(portfolio, 1000.0)
+        pd.testing.assert_frame_equal(
+            portfolio_df.reset_index(drop=True), expected
+        )
+        assert cash == 1000.0
+
+
+def test_load_latest_portfolio_state(tmp_path):
+    data = pd.DataFrame(
+        [
+            {
+                "Date": "2023-01-01",
+                "Ticker": "ABC",
+                "Shares": 10,
+                "Cost Basis": 5,
+                "Stop Loss": 3,
+                "Current Price": 5,
+                "Total Value": 50,
+                "PnL": 0,
+                "Action": "HOLD",
+                "Cash Balance": "",
+                "Total Equity": "",
+            },
+            {
+                "Date": "2023-01-01",
+                "Ticker": "TOTAL",
+                "Shares": "",
+                "Cost Basis": "",
+                "Stop Loss": "",
+                "Current Price": "",
+                "Total Value": 50,
+                "PnL": 0,
+                "Action": "",
+                "Cash Balance": 1000,
+                "Total Equity": 1050,
+            },
+            {
+                "Date": "2023-01-02",
+                "Ticker": "ABC",
+                "Shares": 10,
+                "Cost Basis": 5,
+                "Stop Loss": 3,
+                "Current Price": 5,
+                "Total Value": 50,
+                "PnL": 0,
+                "Action": "HOLD",
+                "Cash Balance": "",
+                "Total Equity": "",
+            },
+            {
+                "Date": "2023-01-02",
+                "Ticker": "TOTAL",
+                "Shares": "",
+                "Cost Basis": "",
+                "Stop Loss": "",
+                "Current Price": "",
+                "Total Value": 50,
+                "PnL": 0,
+                "Action": "",
+                "Cash Balance": 1005,
+                "Total Equity": 1055,
+            },
+        ]
+    )
+    csv_path = tmp_path / "portfolio.csv"
+    data.to_csv(csv_path, index=False)
+
+    holdings, cash = trading_script.load_latest_portfolio_state(str(csv_path))
+
+    assert holdings == [
+        {
+            "Ticker": "ABC",
+            "Stop Loss": 3,
+            "buy_price": 5,
+            "shares": 10,
+            "cost_basis": 50,
+        }
+    ]
+    assert cash == 1005.0