asodijasoidjasoidj

paperone/client.py

@@ -1,6 +1,6 @@
 from datetime import datetime, timedelta
 from .taapi import TaapiClient
-from .data import TickerData
+from .data import TickerOHLCV
 import yfinance as yf
 import pandas as pd
 
@@ -10,7 +10,7 @@ class Client:
         self._taapi = TaapiClient(taapi_key)
 
     @staticmethod
-    def ticker_data_for(ticker: str, date: datetime) -> TickerData | None:
+    def ticker_data_for(ticker: str, date: datetime) -> TickerOHLCV | None:
         # Set end date to next day to ensure we get the target date
         start_date = date.strftime("%Y-%m-%d")
         end_date = (date + timedelta(days=1)).strftime("%Y-%m-%d")
@@ -60,7 +60,7 @@ class Client:
             # Calculate average price
             avg = (high + low) / 2.0
 
-            return TickerData(
+            return TickerOHLCV(
                 date=date,
                 open=round(open_price, 2),
                 high=round(high, 2),

paperone/entities.py

@@ -1,56 +1,13 @@
 from dataclasses import dataclass, field
 from datetime import datetime
 from typing import List
-
+from .models import TickerOHLCV
-
-@dataclass
-class TickerData:
-    date: datetime
-    open: float
-    close: float
-    low: float
-    high: float
-    avg: float
-    volume: int
 
 
 @dataclass
 class TimeSeriesTickerData:
     ticker: str
-    series: List[TickerData] = field(default_factory=list)
+    series: List[TickerOHLCV] = field(default_factory=list)
-
-    @staticmethod
-    def build_time_series_ticker_data(
-        ticker: str, all_data: List[TickerData]
-    ):
-        ordered = sorted({d.date: d for d in all_data}.values(), key=lambda d: d.date)
-
-        return TimeSeriesTickerData(
-            ticker=ticker,
-            series=ordered,
-        )
-
-    def get_by_date(self, dt: datetime) -> TickerData | None:
-        for d in self.series:
-            if d.date == dt:
-                return d
-
-        return None
-
-    def get_latest(self) -> TickerData | None:
-        if not self.series:
-            return None
-
-        return self.series[-1]
-
-    def get_range(self, start_date: datetime, end_date: datetime) -> List[TickerData]:
-        return [d for d in self.series if start_date <= d.date <= end_date]
-
-    @property
-    def target_date_data(self) -> TickerData | None:
-        """Return the most recent TickerData (by convention, the target date tick)."""
-        return self.get_latest()
-
 
 
 @dataclass

paperone/models.py

@@ -0,0 +1,86 @@
+from sqlmodel import SQLModel, Field, Relationship
+from datetime import datetime
+from typing import Optional
+
+
+class TickerOHLCV(SQLModel, table=True):
+    __tablename__ = "ohlcv"
+
+    ticker: str = Field(primary_key=True)
+    date: datetime = Field(primary_key=True, index=True)
+    open: float
+    close: float
+    low: float
+    high: float
+    avg: float
+    volume: int
+
+    indicators: Optional["IndicatorsData"] = Relationship(
+        back_populates="ohlcv",
+        sa_relationship_kwargs={
+            "foreign_keys": "[IndicatorsData.ticker, IndicatorsData.date]",
+            "uselist": False,
+        },
+    )
+
+
+class IndicatorsData(SQLModel, table=True):
+    __tablename__ = "indicators"
+
+    ticker: str = Field(foreign_key="ohlcv.ticker", primary_key=True)
+    date: datetime = Field(foreign_key="ohlcv.date", primary_key=True, index=True)
+
+    # ========================================================================
+    # MOMENTUM INDICATORS (Trend Direction & Strength)
+    # ========================================================================
+    rsi_14: float  # Standard 14-period RSI
+    rsi_20: float  # Longer 20-period RSI for smoother signal
+    macd_line: float  # MACD line (12 EMA - 26 EMA)
+    macd_signal: float  # Signal line (9-period EMA of MACD)
+    macd_histogram: float  # Histogram (MACD - Signal), shows momentum strength
+    stoch_k: float  # Fast stochastic %K (14-period)
+    stoch_d: float  # Slow stochastic %D (3-period SMA of %K)
+
+    # ========================================================================
+    # VOLATILITY INDICATORS (Price Dispersion & Risk)
+    # ========================================================================
+    bb_upper: float  # Upper Bollinger Band (SMA + 2*std)
+    bb_middle: float  # Middle band (20-period SMA)
+    bb_lower: float  # Lower Bollinger Band (SMA - 2*std)
+    bb_width: float  # Band width (upper - lower), measures volatility magnitude
+    bb_percent: float  # %B indicator: (close - lower) / (upper - lower)
+    atr_14: float  # 14-period Average True Range
+
+    # ========================================================================
+    # TREND INDICATORS (Trend Presence & Sustainability)
+    # ========================================================================
+    adx_14: float  # 14-period ADX (trend strength)
+    di_plus: float  # +DI (bullish directional indicator)
+    di_minus: float  # -DI (bearish directional indicator)
+    sar: float  # Current Parabolic SAR level
+
+    # ========================================================================
+    # VOLUME INDICATORS (Institutional Participation)
+    # ========================================================================
+    obv: float  # On-Balance Volume cumulative total
+    obv_sma_20: float  # 20-day SMA of OBV (trend confirmation)
+    volume_roc_5: float  # 5-day volume rate of change (%)
+
+    # ========================================================================
+    # SUPPORT/RESISTANCE INDICATORS (Key Price Levels)
+    # ========================================================================
+    fib_236: float  # 23.6% Fibonacci retracement level
+    fib_382: float  # 38.2% Fibonacci retracement level
+    fib_618: float  # 61.8% Fibonacci retracement level (golden ratio)
+    pivot_point: float  # Standard pivot point (High + Low + Close) / 3
+    resistance_1: float  # First resistance level (R1)
+    support_1: float  # First support level (S1)
+
+    # ========================================================================
+    # MARKET REGIME INDICATORS (Market Condition Classification)
+    # ========================================================================
+    cci_20: float  # 20-period Commodity Channel Index
+    williams_r_14: float  # 14-period Williams %R
+
+    # One-to-one relationship back to TickerOHLCV
+    ohlcv: TickerOHLCV = Relationship(back_populates="indicators")