From a06dcab7f9d1b718ac5277cb7d4fb4cfcc8c6a6a Mon Sep 17 00:00:00 2001
From: Giulio De Pasquale <git@depasquale.giugl.io>
Date: Wed, 15 Oct 2025 19:03:28 +0100
Subject: [PATCH] mbleh

---
 paperone/client.py | 102 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 102 insertions(+)
 create mode 100644 paperone/client.py

diff --git a/paperone/client.py b/paperone/client.py
new file mode 100644
index 0000000..1a380e9
--- /dev/null
+++ b/paperone/client.py
@@ -0,0 +1,102 @@
+from datetime import datetime, timedelta
+from .taapi import TaapiClient
+from typing import List, Dict
+import yfinance as yf
+import pandas as pd
+from dataclasses import dataclass
+
+
+@dataclass
+class TickerData:
+    name: str
+    date: datetime
+    open: float
+    close: float
+    low: float
+    high: float
+    avg: float
+    volume: int
+
+
+@dataclass
+class TimeSeriesFeatures:
+    """Holds time-series data for a ticker with multiple lookback windows"""
+
+    ticker: str
+    target_date: datetime
+    current_day: TickerData
+    vix_current: TickerData
+
+    past_30d: List[TickerData]  # Previous 30 trading days
+
+
+class Client:
+    def __init__(self, taapi_key: str):
+        self._taapi = TaapiClient(taapi_key)
+
+    @staticmethod
+    def ticker_data_for(ticker: str, date: datetime) -> TickerData | 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")
+
+        try:
+            data = yf.download(
+                ticker,
+                start=start_date,
+                end=end_date,
+                auto_adjust=True,
+                progress=False,
+            )
+
+            if data.empty:
+                return None
+
+            row = data.iloc[0]
+
+            open_price = (
+                float(row["Open"].iloc[0])
+                if isinstance(row["Open"], pd.Series)
+                else float(row["Open"])
+            )
+            high = (
+                float(row["High"].iloc[0])
+                if isinstance(row["High"], pd.Series)
+                else float(row["High"])
+            )
+            low = (
+                float(row["Low"].iloc[0])
+                if isinstance(row["Low"], pd.Series)
+                else float(row["Low"])
+            )
+            close = (
+                float(row["Close"].iloc[0])
+                if isinstance(row["Close"], pd.Series)
+                else float(row["Close"])
+            )
+            volume = (
+                int(row["Volume"].iloc[0])
+                if isinstance(row["Volume"], pd.Series)
+                else int(row["Volume"])
+                if "Volume" in row
+                else 0
+            )
+
+            # Calculate average price
+            avg = (high + low) / 2.0
+
+            return TickerData(
+                name=ticker,
+                date=date,
+                open=round(open_price, 2),
+                high=round(high, 2),
+                low=round(low, 2),
+                close=round(close, 2),
+                avg=round(avg, 2),
+                volume=volume,
+            )
+
+        except Exception as e:
+            print(f"Error fetching data for {ticker} on {start_date}: {str(e)}")
+
+            return None