feat(indicators): add indicator service and related dataclasses

paperone/database.py

@@ -33,7 +33,8 @@ class TradingDataCRUD:
     @contextmanager
     def get_session(self):
         """Context manager for database sessions with automatic cleanup."""
-        session = Session(self.engine)
+        session = Session(self.engine, expire_on_commit=False)
+        
         try:
             yield session
             session.commit()

paperone/indicators.py

@@ -0,0 +1,360 @@
+import talib
+import numpy as np
+from typing import List
+from datetime import datetime, timedelta
+from .models import (
+    TickerOHLCV,
+    IndicatorsData,
+    MarketRegimeIndicators,
+    MomentumIndicators,
+    VolatilityIndicators,
+    VolumeIndicators,
+    SupportResistanceIndicators,
+    TrendIndicators,
+    OHLCVArrays,
+)
+from .database import TradingDataCRUD
+
+
+class IndicatorService:
+    """
+    Middleware service for calculating technical indicators from stored OHLCV data.
+
+    This service:
+    1. Fetches historical OHLCV data from the database
+    2. Calculates all 29 technical indicators using TA-Lib
+    3. Returns strongly-typed IndicatorsData ready to be saved
+
+    All calculations are done locally without external API calls.
+    """
+
+    def __init__(self, crud: TradingDataCRUD):
+        """
+        Initialize the indicator service with database access.
+
+        Args:
+            crud: TradingDataCRUD instance for database operations
+        """
+        self._crud: TradingDataCRUD = crud
+
+    def calculate_indicators_for_date(
+        self, ticker: str, target_date: datetime, lookback_days: int = 50
+    ) -> IndicatorsData | None:
+        """
+        Calculate all indicators for a ticker on a specific date using stored OHLCV data.
+
+        Args:
+            ticker: Stock ticker symbol
+            target_date: Date to calculate indicators for
+            lookback_days: Number of historical days needed for calculations (default: 50)
+
+        Returns:
+            IndicatorsData instance ready to save to database, or None if insufficient data
+        """
+        # Fetch historical OHLCV data from database
+        start_date: datetime = target_date - timedelta(
+            days=lookback_days * 2
+        )  # Buffer for weekends
+        ohlcv_records: List[TickerOHLCV] = self._crud.get_ohlcv_range(
+            ticker=ticker, start_date=start_date, end_date=target_date
+        )
+
+        # Verify we have enough data
+        if len(ohlcv_records) < 30:
+            return None
+
+        # Convert to numpy arrays for TA-Lib
+        arrays: OHLCVArrays = self._convert_to_arrays(ohlcv_records)
+
+        # Calculate all indicator categories
+        momentum: MomentumIndicators = self._calculate_momentum(arrays)
+        volatility: VolatilityIndicators = self._calculate_volatility(arrays)
+        trend: TrendIndicators = self._calculate_trend(arrays)
+        volume: VolumeIndicators = self._calculate_volume(arrays)
+        support_resistance: SupportResistanceIndicators = (
+            self._calculate_support_resistance(arrays)
+        )
+        market_regime: MarketRegimeIndicators = self._calculate_market_regime(arrays)
+
+        # Build and return strongly-typed IndicatorsData
+        return IndicatorsData(
+            ticker=ticker,
+            date=target_date,
+
+            # Momentum (7 indicators)
+             
+            rsi_14=momentum.rsi_14,
+            rsi_20=momentum.rsi_20,
+            macd_line=momentum.macd_line,
+            macd_signal=momentum.macd_signal,
+            macd_histogram=momentum.macd_histogram,
+            stoch_k=momentum.stoch_k,
+            stoch_d=momentum.stoch_d,
+
+            # Volatility (6 indicators)
+
+            bb_upper=volatility.bb_upper,
+            bb_middle=volatility.bb_middle,
+            bb_lower=volatility.bb_lower,
+            bb_width=volatility.bb_width,
+            bb_percent=volatility.bb_percent,
+            atr_14=volatility.atr_14,
+
+            # Trend (4 indicators)
+             
+            adx_14=trend.adx_14,
+            di_plus=trend.di_plus,
+            di_minus=trend.di_minus,
+            sar=trend.sar,
+
+            # Volume (3 indicators)
+
+            obv=volume.obv,
+            obv_sma_20=volume.obv_sma_20,
+            volume_roc_5=volume.volume_roc_5,
+
+            # Support/Resistance (6 indicators)
+
+            fib_236=support_resistance.fib_236,
+            fib_382=support_resistance.fib_382,
+            fib_618=support_resistance.fib_618,
+            pivot_point=support_resistance.pivot_point,
+            resistance_1=support_resistance.resistance_1,
+            support_1=support_resistance.support_1,
+
+            # Market Regime (2 indicators)
+
+            cci_20=market_regime.cci_20,
+            williams_r_14=market_regime.williams_r_14,
+        )
+
+    def calculate_and_save_indicators(
+        self, ticker: str, target_date: datetime
+    ) -> IndicatorsData | None:
+        """
+        Calculate indicators and immediately save them to the database.
+
+        Args:
+            ticker: Stock ticker symbol
+            target_date: Date to calculate indicators for
+
+        Returns:
+            Saved IndicatorsData instance or None if calculation failed
+        """
+        indicators: IndicatorsData | None = self.calculate_indicators_for_date(
+            ticker=ticker, target_date=target_date
+        )
+
+        if indicators is None:
+            return None
+
+        # Upsert (insert or update if exists)
+        return self._crud.upsert_indicators(indicators)
+
+    def bulk_calculate_indicators(
+        self, ticker: str, dates: List[datetime]
+    ) -> List[IndicatorsData]:
+        """
+        Calculate indicators for multiple dates efficiently.
+
+        Args:
+            ticker: Stock ticker symbol
+            dates: List of dates to calculate indicators for
+
+        Returns:
+            List of IndicatorsData instances (may be less than input if some failed)
+        """
+        results: List[IndicatorsData] = []
+
+        for date in dates:
+            indicators: IndicatorsData | None = self.calculate_indicators_for_date(
+                ticker=ticker, target_date=date
+            )
+            if indicators is not None:
+                results.append(indicators)
+
+        return results
+
+    def bulk_calculate_and_save_indicators(
+        self, ticker: str, dates: List[datetime]
+    ) -> int:
+        """
+        Calculate and save indicators for multiple dates efficiently.
+
+        Args:
+            ticker: Stock ticker symbol
+            dates: List of dates to calculate indicators for
+
+        Returns:
+            Number of indicator records successfully saved
+        """
+        indicators_list: List[IndicatorsData] = self.bulk_calculate_indicators(
+            ticker=ticker, dates=dates
+        )
+
+        if not indicators_list:
+            return 0
+
+        return self._crud.create_indicators_bulk(indicators_list)
+
+    # ========================================================================
+    # PRIVATE: Data Conversion
+    # ========================================================================
+
+    @staticmethod
+    def _convert_to_arrays(ohlcv_records: List[TickerOHLCV]) -> "OHLCVArrays":
+        """Convert list of TickerOHLCV records to numpy arrays for TA-Lib."""
+        return OHLCVArrays(
+            open=np.array([r.open for r in ohlcv_records], dtype=np.float64),
+            high=np.array([r.high for r in ohlcv_records], dtype=np.float64),
+            low=np.array([r.low for r in ohlcv_records], dtype=np.float64),
+            close=np.array([r.close for r in ohlcv_records], dtype=np.float64),
+            volume=np.array([r.volume for r in ohlcv_records], dtype=np.float64),
+        )
+
+    # ========================================================================
+    # PRIVATE: Indicator Calculations
+    # ========================================================================
+
+    @staticmethod
+    def _calculate_momentum(arrays: "OHLCVArrays") -> "MomentumIndicators":
+        """Calculate momentum indicators (RSI, MACD, Stochastic)."""
+        rsi_14: float = float(talib.RSI(arrays.close, timeperiod=14)[-1])
+        rsi_20: float = float(talib.RSI(arrays.close, timeperiod=20)[-1])
+
+        macd_line_arr, macd_signal_arr, macd_hist_arr = talib.MACD(
+            arrays.close, fastperiod=12, slowperiod=26, signalperiod=9
+        )
+
+        stoch_k_arr, stoch_d_arr = talib.STOCH(
+            arrays.high,
+            arrays.low,
+            arrays.close,
+            fastk_period=14,
+            slowk_period=3,
+            slowd_period=3,
+        )
+
+        return MomentumIndicators(
+            rsi_14=round(rsi_14, 2),
+            rsi_20=round(rsi_20, 2),
+            macd_line=round(float(macd_line_arr[-1]), 4),
+            macd_signal=round(float(macd_signal_arr[-1]), 4),
+            macd_histogram=round(float(macd_hist_arr[-1]), 4),
+            stoch_k=round(float(stoch_k_arr[-1]), 2),
+            stoch_d=round(float(stoch_d_arr[-1]), 2),
+        )
+
+    @staticmethod
+    def _calculate_volatility(arrays: "OHLCVArrays") -> "VolatilityIndicators":
+        """Calculate volatility indicators (Bollinger Bands, ATR)."""
+        bb_upper_arr, bb_middle_arr, bb_lower_arr = talib.BBANDS(
+            arrays.close, timeperiod=20, nbdevup=2, nbdevdn=2
+        )
+
+        bb_upper: float = float(bb_upper_arr[-1])
+        bb_middle: float = float(bb_middle_arr[-1])
+        bb_lower: float = float(bb_lower_arr[-1])
+        bb_width: float = bb_upper - bb_lower
+        bb_percent: float = (
+            (arrays.close[-1] - bb_lower) / (bb_upper - bb_lower)
+            if bb_width > 0
+            else 0.5
+        )
+
+        atr_14: float = float(
+            talib.ATR(arrays.high, arrays.low, arrays.close, timeperiod=14)[-1]
+        )
+
+        return VolatilityIndicators(
+            bb_upper=round(bb_upper, 2),
+            bb_middle=round(bb_middle, 2),
+            bb_lower=round(bb_lower, 2),
+            bb_width=round(bb_width, 2),
+            bb_percent=round(bb_percent, 4),
+            atr_14=round(atr_14, 2),
+        )
+
+    @staticmethod
+    def _calculate_trend(arrays: "OHLCVArrays") -> "TrendIndicators":
+        """Calculate trend indicators (ADX, DI+, DI-, SAR)."""
+        adx_14: float = float(
+            talib.ADX(arrays.high, arrays.low, arrays.close, timeperiod=14)[-1]
+        )
+        di_plus: float = float(
+            talib.PLUS_DI(arrays.high, arrays.low, arrays.close, timeperiod=14)[-1]
+        )
+        di_minus: float = float(
+            talib.MINUS_DI(arrays.high, arrays.low, arrays.close, timeperiod=14)[-1]
+        )
+        sar: float = float(
+            talib.SAR(arrays.high, arrays.low, acceleration=0.02, maximum=0.2)[-1]
+        )
+
+        return TrendIndicators(
+            adx_14=round(adx_14, 2),
+            di_plus=round(di_plus, 2),
+            di_minus=round(di_minus, 2),
+            sar=round(sar, 2),
+        )
+
+    @staticmethod
+    def _calculate_volume(arrays: "OHLCVArrays") -> "VolumeIndicators":
+        """Calculate volume indicators (OBV, Volume ROC)."""
+        obv_arr: np.ndarray = talib.OBV(arrays.close, arrays.volume)
+        obv: float = float(obv_arr[-1])
+        obv_sma_20: float = float(talib.SMA(obv_arr, timeperiod=20)[-1])
+        volume_roc_5: float = float(talib.ROC(arrays.volume, timeperiod=5)[-1])
+
+        return VolumeIndicators(
+            obv=round(obv, 0),
+            obv_sma_20=round(obv_sma_20, 0),
+            volume_roc_5=round(volume_roc_5, 2),
+        )
+
+    @staticmethod
+    def _calculate_support_resistance(
+        arrays: "OHLCVArrays",
+    ) -> "SupportResistanceIndicators":
+        """Calculate support/resistance levels (Fibonacci, Pivot Points)."""
+        # Fibonacci retracements (last 30 days)
+        lookback: int = min(30, len(arrays.high))
+        max_price: float = float(np.max(arrays.high[-lookback:]))
+        min_price: float = float(np.min(arrays.low[-lookback:]))
+        diff: float = max_price - min_price
+
+        fib_236: float = max_price - (diff * 0.236)
+        fib_382: float = max_price - (diff * 0.382)
+        fib_618: float = max_price - (diff * 0.618)
+
+        # Pivot points (previous day's data)
+        high_prev: float = float(arrays.high[-1])
+        low_prev: float = float(arrays.low[-1])
+        close_prev: float = float(arrays.close[-1])
+
+        pivot_point: float = (high_prev + low_prev + close_prev) / 3
+        resistance_1: float = (2 * pivot_point) - low_prev
+        support_1: float = (2 * pivot_point) - high_prev
+
+        return SupportResistanceIndicators(
+            fib_236=round(fib_236, 2),
+            fib_382=round(fib_382, 2),
+            fib_618=round(fib_618, 2),
+            pivot_point=round(pivot_point, 2),
+            resistance_1=round(resistance_1, 2),
+            support_1=round(support_1, 2),
+        )
+
+    @staticmethod
+    def _calculate_market_regime(arrays: "OHLCVArrays") -> "MarketRegimeIndicators":
+        """Calculate market regime indicators (CCI, Williams %R)."""
+        cci_20: float = float(
+            talib.CCI(arrays.high, arrays.low, arrays.close, timeperiod=20)[-1]
+        )
+        williams_r_14: float = float(
+            talib.WILLR(arrays.high, arrays.low, arrays.close, timeperiod=14)[-1]
+        )
+
+        return MarketRegimeIndicators(
+            cci_20=round(cci_20, 2), williams_r_14=round(williams_r_14, 2)
+        )

paperone/models.py

@@ -1,6 +1,8 @@
 from sqlmodel import SQLModel, Field, Relationship, ForeignKeyConstraint
+from dataclasses import dataclass
 from datetime import datetime
 from typing import Optional
+import numpy as np
 
 
 class TickerOHLCV(SQLModel, table=True):
@@ -84,3 +86,78 @@ class IndicatorsData(SQLModel, table=True):
 
     # One-to-one relationship back to TickerOHLCV
     ohlcv: TickerOHLCV = Relationship(back_populates="indicators")
+
+
+@dataclass(frozen=True)
+class OHLCVArrays:
+    """Numpy arrays of OHLCV data for TA-Lib calculations."""
+
+    open: np.ndarray
+    high: np.ndarray
+    low: np.ndarray
+    close: np.ndarray
+    volume: np.ndarray
+
+
+@dataclass(frozen=True)
+class MomentumIndicators:
+    """Momentum indicator values (RSI, MACD, Stochastic)."""
+
+    rsi_14: float
+    rsi_20: float
+    macd_line: float
+    macd_signal: float
+    macd_histogram: float
+    stoch_k: float
+    stoch_d: float
+
+
+@dataclass(frozen=True)
+class VolatilityIndicators:
+    """Volatility indicator values (Bollinger Bands, ATR)."""
+
+    bb_upper: float
+    bb_middle: float
+    bb_lower: float
+    bb_width: float
+    bb_percent: float
+    atr_14: float
+
+
+@dataclass(frozen=True)
+class TrendIndicators:
+    """Trend indicator values (ADX, DI+, DI-, SAR)."""
+
+    adx_14: float
+    di_plus: float
+    di_minus: float
+    sar: float
+
+
+@dataclass(frozen=True)
+class VolumeIndicators:
+    """Volume indicator values (OBV, Volume ROC)."""
+
+    obv: float
+    obv_sma_20: float
+    volume_roc_5: float
+
+
+@dataclass(frozen=True)
+class SupportResistanceIndicators:
+    """Support/Resistance levels (Fibonacci, Pivot Points)."""
+
+    fib_236: float
+    fib_382: float
+    fib_618: float
+    pivot_point: float
+    resistance_1: float
+    support_1: float
+
+
+@dataclass(frozen=True)
+class MarketRegimeIndicators:
+    """Market regime indicators (CCI, Williams %R)."""
+
+    cci_20: float
+    williams_r_14: float

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -70,6 +70,27 @@ charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
+[[package]]
+name = "build"
+version = "1.3.0"
+description = "A simple, correct Python build frontend"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "build-1.3.0-py3-none-any.whl", hash = "sha256:7145f0b5061ba90a1500d60bd1b13ca0a8a4cebdd0cc16ed8adf1c0e739f43b4"},
+    {file = "build-1.3.0.tar.gz", hash = "sha256:698edd0ea270bde950f53aed21f3a0135672206f3911e0176261a31e0e07b397"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "os_name == \"nt\""}
+packaging = ">=19.1"
+pyproject_hooks = "*"
+
+[package.extras]
+uv = ["uv (>=0.1.18)"]
+virtualenv = ["virtualenv (>=20.11) ; python_version < \"3.10\"", "virtualenv (>=20.17) ; python_version >= \"3.10\" and python_version < \"3.14\"", "virtualenv (>=20.31) ; python_version >= \"3.14\""]
+
 [[package]]
 name = "certifi"
 version = "2025.10.5"
@@ -290,7 +311,7 @@ description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 groups = ["main"]
-markers = "platform_system == \"Windows\" or sys_platform == \"win32\""
+markers = "platform_system == \"Windows\" or sys_platform == \"win32\" or os_name == \"nt\""
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
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@@ -326,6 +347,88 @@ dev = ["charset_normalizer (>=3.3.2,<4.0)", "coverage (>=6.4.1,<7.0)", "cryptogr
 extra = ["lxml_html_clean", "markdownify (>=1.1.0)", "readability-lxml (>=0.8.1)"]
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+[[package]]
+name = "cython"
+version = "3.1.4"
+description = "The Cython compiler for writing C extensions in the Python language."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
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 test = ["beautifulsoup4", "flake8", "pytest", "pytest-cov"]
 
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 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
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+]
+
+[package.dependencies]
+build = "*"
+cython = "*"
+numpy = "*"
+
 [[package]]
 name = "toolz"
 version = "1.0.0"
@@ -1647,4 +1835,4 @@ repair = ["scipy (>=1.6.3)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.13"
-content-hash = "f8ab199e4a4efc383caa5691f31314d4cdecc6564e120b4024ce3eb1711d00f5"
+content-hash = "c9a5936e446cf6234585a330d4fc3888dd4a8c8db505bbc9e78d454cfe864569"

pyproject.toml

@@ -15,7 +15,8 @@ dependencies = [
     "yfinance (>=0.2.66,<0.3.0)",
     "ipython (>=9.6.0,<10.0.0)",
     "pandas-market-calendars (>=5.1.1,<6.0.0)",
-    "sqlmodel (>=0.0.27,<0.0.28)"
+    "sqlmodel (>=0.0.27,<0.0.28)",
+    "ta-lib (>=0.6.7,<0.7.0)"
 ]