README.md

@@ -14,7 +14,7 @@ ingestion/edgar_poller.py   ← polls every 10 min, dedupes by accession
 ingestion/form4_parser.py   ← parses XML, detects 10b5-1 plans
       │
       ▼
-db/schema.sql + db/db.py    ← SQLite (WAL mode): filings + signals tables
+db/models.py + db/db.py     ← SQLAlchemy ORM: filings, signals, price_cache tables
       │
       ▼
 signals/filter_engine.py    ← buy-only, exclude 10b5-1, min $50k, role-weighted scoring
@@ -80,7 +80,9 @@ Role weights: CEO 3.0 · CFO/President 2.5 · COO 2.0 · Director 1.5 · VP 1.2
 
 ## Backtesting
 
-The backtest loads signals from the SQLite DB and fetches OHLC data via `yfinance` on demand (no local price cache). Entry price is the closing price on the first trading day on or after the signal date; exit price is the closing price on the last trading day before or on the exit date. Raw XML filings are cached in `DATA_DIR` (`data/filings/`) by accession number.
+The backtest loads signals from the DB and fetches OHLC data via `yfinance`. Prices are cached in the `price_cache` table — completed date ranges are served entirely from the DB on repeat runs, avoiding redundant network calls. Entry price is the closing price on the first trading day on or after the signal date; exit price is the closing price on the last trading day before or on the exit date. Raw XML filings are cached in `DATA_DIR` (`data/filings/`) by accession number.
+
+The EDGAR poller also skips fetching XML for filings older than the newest `filed_date` already stored in the DB, so incremental runs only process truly new filings.
 
 Metrics reported: win rate, average return, average alpha vs SPY, Sharpe ratio.
 
@@ -95,8 +97,8 @@ Positions are tracked in the `signals` table. When a trade is executed, `execute
 | `config.py` | All thresholds and env-var loading |
 | `ingestion/edgar_poller.py` | EDGAR Atom feed polling and deduplication |
 | `ingestion/form4_parser.py` | Form 4 XML → structured dict; 10b5-1 detection |
-| `db/schema.sql` | SQLite schema (`filings`, `signals`) |
+| `db/models.py` | SQLAlchemy ORM models (`Filing`, `Signal`, `PriceCache`) |
-| `db/db.py` | DB access layer |
+| `db/db.py` | DB access layer (SQLAlchemy sessions) |
 | `signals/filter_engine.py` | Filing → signal pipeline |
 | `signals/cluster_detector.py` | Cluster detection from DB |
 | `alerts/slack_alert.py` | Slack webhook alert |
@@ -107,4 +109,4 @@ Positions are tracked in the `signals` table. When a trade is executed, `execute
 ## Requirements
 
 - Python 3.11+
-- See `requirements.txt`: `requests`, `lxml`, `yfinance`, `python-dotenv`, `alpaca-trade-api`
+- See `requirements.txt`: `requests`, `lxml`, `yfinance`, `python-dotenv`, `alpaca-trade-api`, `sqlalchemy`

backtest/backtest.py

@@ -3,44 +3,67 @@ import math
 from datetime import datetime, timedelta
 
 import config
+from db.db import get_cached_prices, get_signals_for_backtest, upsert_prices
 
 logger = logging.getLogger(__name__)
 
 
-def _load_signals_from_db(db_path: str, min_score: float, min_cluster_size: int) -> list[dict]:
+def _fetch_prices(ticker: str, start: datetime, end: datetime) -> dict[str, float]:
-    import sqlite3
+    try:
-    conn = sqlite3.connect(db_path)
+        import yfinance as yf
-    conn.row_factory = sqlite3.Row
+    except ImportError:
-    rows = conn.execute(
+        raise ImportError("yfinance not installed. Run: pip install yfinance")
-        """
+
-        SELECT s.*, f.role FROM signals s
+    start_str = start.strftime("%Y-%m-%d")
-        LEFT JOIN filings f ON f.ticker = s.ticker AND f.transaction_date = s.trigger_date
+    end_str = (end + timedelta(days=5)).strftime("%Y-%m-%d")
-        WHERE s.score >= ? AND s.cluster_size >= ?
+
-        """,
+    cached = get_cached_prices(ticker, start_str, end_str)
-        (min_score, min_cluster_size),
+
-    ).fetchall()
+    today = datetime.utcnow().strftime("%Y-%m-%d")
-    conn.close()
+    range_is_complete = end_str < today
-    return [dict(r) for r in rows]
+
+    if range_is_complete and cached:
+        return cached
+
+    data = yf.download(
+        ticker,
+        start=start_str,
+        end=end_str,
+        progress=False,
+        auto_adjust=True,
+    )
+    if data.empty:
+        return cached
+
+    fetched: dict[str, float] = {}
+    for ts, close_val in data["Close"].items():
+        date_key = ts.to_pydatetime().replace(tzinfo=None).strftime("%Y-%m-%d")
+        fetched[date_key] = float(close_val)
+
+    new_prices = {k: v for k, v in fetched.items() if k not in cached}
+    if new_prices:
+        upsert_prices(ticker, new_prices)
+
+    cached.update(fetched)
+    return cached
 
 
-def _first_close_on_or_after(price_data, target_date: datetime) -> float:
+def _first_close_on_or_after(prices: dict[str, float], target: datetime) -> float:
-    """Return the closing price on the first trading day on or after target_date."""
+    target_str = target.strftime("%Y-%m-%d")
-    for ts, row in price_data["Close"].items():
+    for date_str in sorted(prices):
-        ts_date = ts.to_pydatetime().replace(tzinfo=None)
+        if date_str >= target_str:
-        if ts_date.date() >= target_date.date():
+            return prices[date_str]
-            return float(row)
+    raise ValueError(f"No price data on or after {target_str}")
-    raise ValueError(f"No price data on or after {target_date.date()}")
 
 
-def _first_close_before(price_data, target_date: datetime) -> float:
+def _first_close_before(prices: dict[str, float], target: datetime) -> float:
-    """Return the closing price on the last trading day before or on target_date."""
+    target_str = target.strftime("%Y-%m-%d")
     result = None
-    for ts, row in price_data["Close"].items():
+    for date_str in sorted(prices):
-        ts_date = ts.to_pydatetime().replace(tzinfo=None)
+        if date_str <= target_str:
-        if ts_date.date() <= target_date.date():
+            result = prices[date_str]
-            result = float(row)
     if result is None:
-        raise ValueError(f"No price data on or before {target_date.date()}")
+        raise ValueError(f"No price data on or before {target_str}")
     return result
 
 
@@ -50,22 +73,15 @@ def run_backtest(
     min_score: float = 0.0,
     min_cluster_size: int = 1,
 ) -> dict:
-    try:
-        import yfinance as yf
-    except ImportError:
-        raise ImportError("yfinance not installed. Run: pip install yfinance")
-
-    db_path = db_path or config.DB_PATH
     holding_days = holding_days or config.HOLDING_PERIOD_DAYS
-
+    signals = get_signals_for_backtest(min_score, min_cluster_size)
-    signals = _load_signals_from_db(db_path, min_score, min_cluster_size)
 
     if not signals:
         logger.warning("No signals found matching criteria")
         return {}
 
     results = []
-    spy_cache: dict[tuple, float] = {}
+    spy_cache: dict[str, float] = {}
 
     for signal in signals:
         ticker = signal["ticker"]
@@ -79,38 +95,26 @@ def run_backtest(
         exit_date = entry_date + timedelta(days=holding_days)
 
         try:
-            stock_data = yf.download(
+            prices = _fetch_prices(ticker, entry_date, exit_date)
-                ticker,
+            if not prices:
-                start=entry_date.strftime("%Y-%m-%d"),
-                end=(exit_date + timedelta(days=5)).strftime("%Y-%m-%d"),
-                progress=False,
-                auto_adjust=True,
-            )
-            if stock_data.empty:
                 logger.debug(f"No price data for {ticker}")
                 continue
 
-            entry_price = _first_close_on_or_after(stock_data, entry_date)
+            entry_price = _first_close_on_or_after(prices, entry_date)
-            exit_price = _first_close_before(stock_data, exit_date)
+            exit_price = _first_close_before(prices, exit_date)
             stock_return = (exit_price - entry_price) / entry_price
 
         except Exception as e:
             logger.debug(f"Failed to get data for {ticker}: {e}")
             continue
 
-        period_key = (entry_date_str, holding_days)
+        period_key = entry_date_str
         if period_key not in spy_cache:
             try:
-                spy_data = yf.download(
+                spy_prices = _fetch_prices("SPY", entry_date, exit_date)
-                    "SPY",
+                if spy_prices:
-                    start=entry_date.strftime("%Y-%m-%d"),
+                    spy_entry = _first_close_on_or_after(spy_prices, entry_date)
-                    end=(exit_date + timedelta(days=5)).strftime("%Y-%m-%d"),
+                    spy_exit = _first_close_before(spy_prices, exit_date)
-                    progress=False,
-                    auto_adjust=True,
-                )
-                if not spy_data.empty:
-                    spy_entry = _first_close_on_or_after(spy_data, entry_date)
-                    spy_exit = _first_close_before(spy_data, exit_date)
                     spy_cache[period_key] = (spy_exit - spy_entry) / spy_entry
                 else:
                     spy_cache[period_key] = 0.0

db/db.py

@@ -1,146 +1,226 @@
-import sqlite3
-import os
 from datetime import datetime
+from typing import Optional
+
+from sqlalchemy import create_engine, func, select, text, update
+from sqlalchemy.orm import Session
+
 import config
+from db.models import Base, Filing, PriceCache, Signal
 
 
-def get_connection():
+def _engine():
-    conn = sqlite3.connect(config.DB_PATH)
+    url = f"sqlite:///{config.DB_PATH}"
-    conn.row_factory = sqlite3.Row
+    return create_engine(url, connect_args={"check_same_thread": False})
-    conn.execute("PRAGMA journal_mode=WAL")
+
-    conn.execute("PRAGMA foreign_keys=ON")
+
-    return conn
+_ENGINE = None
+
+
+def _get_engine():
+    global _ENGINE
+    if _ENGINE is None:
+        _ENGINE = _engine()
+    return _ENGINE
 
 
 def init_db():
-    schema_path = os.path.join(os.path.dirname(__file__), "schema.sql")
+    engine = _get_engine()
-    with open(schema_path, "r") as f:
+    with engine.connect() as conn:
-        schema = f.read()
+        conn.execute(text("PRAGMA journal_mode=WAL"))
-    conn = get_connection()
+        conn.execute(text("PRAGMA foreign_keys=ON"))
-    conn.executescript(schema)
+    Base.metadata.create_all(engine)
-    conn.commit()
+
-    conn.close()
+
+def _session() -> Session:
+    return Session(_get_engine())
 
 
 def insert_filing(filing: dict) -> bool:
-    conn = get_connection()
+    with _session() as session:
-    try:
+        exists = session.scalar(
-        conn.execute(
+            select(Filing.id).where(Filing.accession_number == filing["accession_number"])
-            """
-            INSERT OR IGNORE INTO filings
-                (accession_number, ticker, cik, insider_name, role,
-                 transaction_date, filed_date, shares, price, total_value,
-                 flag, is_10b51, post_tx_shares)
-            VALUES
-                (:accession_number, :ticker, :cik, :insider_name, :role,
-                 :transaction_date, :filed_date, :shares, :price, :total_value,
-                 :flag, :is_10b51, :post_tx_shares)
-            """,
-            filing,
         )
-        inserted = conn.execute("SELECT changes()").fetchone()[0] > 0
+        if exists is not None:
-        conn.commit()
+            return False
-        return inserted
-    finally:
-        conn.close()
 
-
+        row = Filing(
-def insert_signal(signal: dict) -> int:
+            accession_number=filing["accession_number"],
-    conn = get_connection()
+            ticker=filing.get("ticker"),
-    try:
+            cik=filing.get("cik"),
-        cur = conn.execute(
+            insider_name=filing.get("insider_name"),
-            """
+            role=filing.get("role"),
-            INSERT INTO signals
+            transaction_date=filing.get("transaction_date"),
-                (ticker, trigger_date, cluster_size, total_cluster_value, score)
+            filed_date=filing.get("filed_date"),
-            VALUES
+            shares=filing.get("shares"),
-                (:ticker, :trigger_date, :cluster_size, :total_cluster_value, :score)
+            price=filing.get("price"),
-            """,
+            total_value=filing.get("total_value"),
-            signal,
+            flag=filing.get("flag"),
+            is_10b51=bool(filing.get("is_10b51", False)),
+            post_tx_shares=filing.get("post_tx_shares"),
         )
-        signal_id = cur.lastrowid
+        session.add(row)
-        conn.commit()
+        session.commit()
-        return signal_id
+        return True
-    finally:
-        conn.close()
-
-
-def mark_signal_alerted(signal_id: int):
-    conn = get_connection()
-    try:
-        conn.execute("UPDATE signals SET alerted=1 WHERE id=?", (signal_id,))
-        conn.commit()
-    finally:
-        conn.close()
-
-
-def mark_signal_executed(signal_id: int):
-    conn = get_connection()
-    try:
-        conn.execute(
-            "UPDATE signals SET executed=1, executed_at=? WHERE id=?",
-            (datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ"), signal_id),
-        )
-        conn.commit()
-    finally:
-        conn.close()
-
-
-def mark_signal_closed(signal_id: int):
-    conn = get_connection()
-    try:
-        conn.execute("UPDATE signals SET closed=1 WHERE id=?", (signal_id,))
-        conn.commit()
-    finally:
-        conn.close()
-
-
-def get_unalerted_signals() -> list[dict]:
-    conn = get_connection()
-    try:
-        rows = conn.execute(
-            "SELECT * FROM signals WHERE alerted=0 ORDER BY created_at ASC"
-        ).fetchall()
-        return [dict(r) for r in rows]
-    finally:
-        conn.close()
-
-
-def get_executed_unclosed_signals() -> list[dict]:
-    conn = get_connection()
-    try:
-        rows = conn.execute(
-            "SELECT * FROM signals WHERE executed=1 AND closed=0 AND executed_at IS NOT NULL"
-        ).fetchall()
-        return [dict(r) for r in rows]
-    finally:
-        conn.close()
-
-
-def get_recent_buys_for_ticker(ticker: str, window_days: int) -> list:
-    conn = get_connection()
-    try:
-        rows = conn.execute(
-            """
-            SELECT * FROM filings
-            WHERE ticker=?
-              AND flag='A'
-              AND is_10b51=0
-              AND transaction_date >= date('now', ? || ' days')
-            ORDER BY transaction_date DESC
-            """,
-            (ticker, f"-{window_days}"),
-        ).fetchall()
-        return [dict(r) for r in rows]
-    finally:
-        conn.close()
 
 
 def accession_exists(accession_number: str) -> bool:
-    conn = get_connection()
+    with _session() as session:
-    try:
+        return session.scalar(
-        row = conn.execute(
+            select(Filing.id).where(Filing.accession_number == accession_number)
-            "SELECT 1 FROM filings WHERE accession_number=?", (accession_number,)
+        ) is not None
-        ).fetchone()
+
-        return row is not None
+
-    finally:
+def get_latest_filed_date() -> Optional[str]:
-        conn.close()
+    with _session() as session:
+        return session.scalar(select(func.max(Filing.filed_date)))
+
+
+def insert_signal(signal: dict) -> int:
+    with _session() as session:
+        row = Signal(
+            ticker=signal["ticker"],
+            trigger_date=signal["trigger_date"],
+            cluster_size=signal["cluster_size"],
+            total_cluster_value=signal.get("total_cluster_value", 0.0),
+            score=signal["score"],
+        )
+        session.add(row)
+        session.commit()
+        return row.id
+
+
+def mark_signal_alerted(signal_id: int):
+    with _session() as session:
+        session.execute(
+            update(Signal).where(Signal.id == signal_id).values(alerted=True)
+        )
+        session.commit()
+
+
+def mark_signal_executed(signal_id: int):
+    with _session() as session:
+        session.execute(
+            update(Signal)
+            .where(Signal.id == signal_id)
+            .values(executed=True, executed_at=datetime.utcnow())
+        )
+        session.commit()
+
+
+def mark_signal_closed(signal_id: int):
+    with _session() as session:
+        session.execute(
+            update(Signal).where(Signal.id == signal_id).values(closed=True)
+        )
+        session.commit()
+
+
+def get_unalerted_signals() -> list[dict]:
+    with _session() as session:
+        rows = session.scalars(
+            select(Signal).where(Signal.alerted == False).order_by(Signal.created_at)
+        ).all()
+        return [_signal_to_dict(r) for r in rows]
+
+
+def get_executed_unclosed_signals() -> list[dict]:
+    with _session() as session:
+        rows = session.scalars(
+            select(Signal).where(
+                Signal.executed == True,
+                Signal.closed == False,
+                Signal.executed_at.is_not(None),
+            )
+        ).all()
+        return [_signal_to_dict(r) for r in rows]
+
+
+def get_recent_buys_for_ticker(ticker: str, window_days: int) -> list[dict]:
+    from datetime import timedelta
+
+    cutoff = (datetime.utcnow() - timedelta(days=window_days)).strftime("%Y-%m-%d")
+    with _session() as session:
+        rows = session.scalars(
+            select(Filing)
+            .where(
+                Filing.ticker == ticker,
+                Filing.flag == "A",
+                Filing.is_10b51 == False,
+                Filing.transaction_date >= cutoff,
+            )
+            .order_by(Filing.transaction_date.desc())
+        ).all()
+        return [_filing_to_dict(r) for r in rows]
+
+
+def get_signals_for_backtest(min_score: float, min_cluster_size: int) -> list[dict]:
+    with _session() as session:
+        rows = session.scalars(
+            select(Signal).where(
+                Signal.score >= min_score,
+                Signal.cluster_size >= min_cluster_size,
+            )
+        ).all()
+        return [_signal_to_dict(r) for r in rows]
+
+
+def get_cached_prices(ticker: str, start_date: str, end_date: str) -> dict[str, float]:
+    with _session() as session:
+        rows = session.scalars(
+            select(PriceCache).where(
+                PriceCache.ticker == ticker,
+                PriceCache.date >= start_date,
+                PriceCache.date <= end_date,
+            )
+        ).all()
+        return {r.date: r.close for r in rows}
+
+
+def upsert_prices(ticker: str, prices: dict[str, float]):
+    with _session() as session:
+        for date_str, close in prices.items():
+            existing = session.scalar(
+                select(PriceCache).where(
+                    PriceCache.ticker == ticker,
+                    PriceCache.date == date_str,
+                )
+            )
+            if existing is None:
+                session.add(PriceCache(ticker=ticker, date=date_str, close=close))
+        session.commit()
+
+
+def _filing_to_dict(row: Filing) -> dict:
+    return {
+        "id": row.id,
+        "accession_number": row.accession_number,
+        "ticker": row.ticker,
+        "cik": row.cik,
+        "insider_name": row.insider_name,
+        "role": row.role,
+        "transaction_date": row.transaction_date,
+        "filed_date": row.filed_date,
+        "shares": row.shares,
+        "price": row.price,
+        "total_value": row.total_value,
+        "flag": row.flag,
+        "is_10b51": row.is_10b51,
+        "post_tx_shares": row.post_tx_shares,
+        "created_at": row.created_at.isoformat() if row.created_at else None,
+    }
+
+
+def _signal_to_dict(row: Signal) -> dict:
+    return {
+        "id": row.id,
+        "ticker": row.ticker,
+        "trigger_date": row.trigger_date,
+        "cluster_size": row.cluster_size,
+        "total_cluster_value": row.total_cluster_value,
+        "score": row.score,
+        "alerted": row.alerted,
+        "executed": row.executed,
+        "executed_at": row.executed_at.strftime("%Y-%m-%dT%H:%M:%SZ") if row.executed_at else None,
+        "closed": row.closed,
+        "created_at": row.created_at.isoformat() if row.created_at else None,
+    }

db/models.py

@@ -0,0 +1,81 @@
+from datetime import datetime
+
+from sqlalchemy import (
+    Boolean,
+    Column,
+    DateTime,
+    Float,
+    Index,
+    Integer,
+    String,
+    Text,
+    UniqueConstraint,
+)
+from sqlalchemy.orm import DeclarativeBase
+
+
+class Base(DeclarativeBase):
+    pass
+
+
+class Filing(Base):
+    __tablename__ = "filings"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    accession_number = Column(String, unique=True, nullable=False)
+    ticker = Column(String)
+    cik = Column(String)
+    insider_name = Column(String)
+    role = Column(String)
+    transaction_date = Column(String)
+    filed_date = Column(String)
+    shares = Column(Float)
+    price = Column(Float)
+    total_value = Column(Float)
+    flag = Column(String)
+    is_10b51 = Column(Boolean, default=False)
+    post_tx_shares = Column(Float)
+    created_at = Column(DateTime, default=datetime.utcnow)
+
+    __table_args__ = (
+        Index("idx_filings_ticker", "ticker"),
+        Index("idx_filings_transaction_date", "transaction_date"),
+        Index("idx_filings_filed_date", "filed_date"),
+    )
+
+
+class Signal(Base):
+    __tablename__ = "signals"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    ticker = Column(String)
+    trigger_date = Column(String)
+    cluster_size = Column(Integer)
+    total_cluster_value = Column(Float)
+    score = Column(Float)
+    alerted = Column(Boolean, default=False)
+    executed = Column(Boolean, default=False)
+    executed_at = Column(DateTime)
+    closed = Column(Boolean, default=False)
+    created_at = Column(DateTime, default=datetime.utcnow)
+
+    __table_args__ = (
+        Index("idx_signals_ticker", "ticker"),
+        Index("idx_signals_alerted", "alerted"),
+        Index("idx_signals_executed", "executed"),
+    )
+
+
+class PriceCache(Base):
+    __tablename__ = "price_cache"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    ticker = Column(String, nullable=False)
+    date = Column(String, nullable=False)
+    close = Column(Float, nullable=False)
+    fetched_at = Column(DateTime, default=datetime.utcnow)
+
+    __table_args__ = (
+        UniqueConstraint("ticker", "date", name="uq_price_cache_ticker_date"),
+        Index("idx_price_cache_ticker_date", "ticker", "date"),
+    )

db/schema.sql

@@ -1,37 +0,0 @@
-CREATE TABLE IF NOT EXISTS filings (
-    id INTEGER PRIMARY KEY AUTOINCREMENT,
-    accession_number TEXT UNIQUE,
-    ticker TEXT,
-    cik TEXT,
-    insider_name TEXT,
-    role TEXT,
-    transaction_date TEXT,
-    filed_date TEXT,
-    shares REAL,
-    price REAL,
-    total_value REAL,
-    flag TEXT,
-    is_10b51 INTEGER DEFAULT 0,
-    post_tx_shares REAL,
-    created_at TEXT DEFAULT (datetime('now'))
-);
-
-CREATE TABLE IF NOT EXISTS signals (
-    id INTEGER PRIMARY KEY AUTOINCREMENT,
-    ticker TEXT,
-    trigger_date TEXT,
-    cluster_size INTEGER,
-    total_cluster_value REAL,
-    score REAL,
-    alerted INTEGER DEFAULT 0,
-    executed INTEGER DEFAULT 0,
-    executed_at TEXT,
-    closed INTEGER DEFAULT 0,
-    created_at TEXT DEFAULT (datetime('now'))
-);
-
-CREATE INDEX IF NOT EXISTS idx_filings_ticker ON filings(ticker);
-CREATE INDEX IF NOT EXISTS idx_filings_transaction_date ON filings(transaction_date);
-CREATE INDEX IF NOT EXISTS idx_signals_ticker ON signals(ticker);
-CREATE INDEX IF NOT EXISTS idx_signals_alerted ON signals(alerted);
-CREATE INDEX IF NOT EXISTS idx_signals_executed ON signals(executed);

ingestion/edgar_poller.py

@@ -7,7 +7,7 @@ from lxml import etree, html
 
 import config
 from ingestion.form4_parser import parse_form4
-from db.db import insert_filing, accession_exists
+from db.db import accession_exists, get_latest_filed_date, insert_filing
 
 logger = logging.getLogger(__name__)
 
@@ -82,7 +82,13 @@ def fetch_and_store_new_filings() -> list[dict]:
         logger.error(f"Failed to fetch EDGAR index: {e}")
         return new_filings
 
+    latest_in_db = get_latest_filed_date()
+
     for _index_url, accession, filed_date in entries:
+        if latest_in_db and filed_date and filed_date < latest_in_db:
+            logger.debug(f"Skipping {accession}: filed_date {filed_date} older than latest in DB {latest_in_db}")
+            continue
+
         if accession_exists(accession):
             continue
 

requirements.txt

@@ -3,3 +3,4 @@ lxml>=5.0.0
 yfinance>=0.2.0
 python-dotenv>=1.0.0
 alpaca-trade-api>=3.0.0
+sqlalchemy>=2.0.0