V2.1.6 (#57)

* update pypi

* latest quotes and tests added

* remove uneccesary code

* update depend

Author: TexasCoding

poetry.lock

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "py-alpaca-api"
-version = "2.1.5"
+version = "2.1.6"
 description = "Python package, for communicating with Alpaca Markets REST API."
 authors = ["TexasCoding <[email protected]>"]
 readme = "README.md"
@@ -14,11 +14,9 @@ python = "^3.12"
 requests = "^2.32.3"
 pendulum = "^3.0.0"
 pandas = "^2.2.2"
-tqdm = "^4.66.4"
 prophet = "^1.1.5"
 beautifulsoup4 = "^4.12.3"
 yfinance = "^0.2.40"
-rich = "^13.7.1"
 
 
 [tool.poetry.group.dev.dependencies]

pyproject.toml

@@ -0,0 +1,32 @@
+from dataclasses import dataclass
+from datetime import datetime
+
+from py_alpaca_api.models.model_utils import KEY_PROCESSORS, extract_class_data
+
+
+@dataclass
+class QuoteModel:
+    symbol: str
+    timestamp: datetime
+    ask: float
+    ask_size: int
+    bid: float
+    bid_size: int
+
+
+############################################
+# Data Class Quote Conversion Functions
+############################################
+def quote_class_from_dict(data_dict: dict) -> QuoteModel:
+    """
+    Args:
+        data_dict: A dictionary containing data for creating an instance of `QuoteModel`.
+
+    Returns:
+        An instance of `QuoteModel` created using the data from `data_dict`.
+
+    Raises:
+        None.
+    """
+    clock_data = extract_class_data(data_dict, KEY_PROCESSORS, QuoteModel)
+    return QuoteModel(**clock_data)

src/py_alpaca_api/models/quote_model.py

@@ -46,4 +46,4 @@ def _initialize_components(
             data_url=data_url, headers=headers, market=market, asset=self.assets
         )
         self.predictor = Predictor(history=self.history, screener=self.screener)
-        self.quotes = LatestQuote(headers=headers)
+        self.latest_quote = LatestQuote(headers=headers)

src/py_alpaca_api/stock/__init__.py

@@ -1,9 +1,8 @@
 import json
 from typing import Dict, List, Optional
 
-import pendulum
-
 from py_alpaca_api.http.requests import Requests
+from py_alpaca_api.models.quote_model import quote_class_from_dict
 
 
 class LatestQuote:
@@ -16,6 +15,18 @@ def get(
         feed: str = "iex",
         currency: str = "USD",
     ) -> dict:
+        if symbol is None or symbol == "":
+            raise ValueError("Symbol is required. Must be a string or list of strings.")
+
+        valid_feeds = ["iex", "sip", "otc"]
+        if feed not in valid_feeds:
+            raise ValueError("Invalid feed, must be one of: 'iex', 'sip', 'otc'")
+
+        if isinstance(symbol, list):
+            symbol = ",".join(symbol).replace(" ", "").upper()
+        else:
+            symbol = symbol.replace(" ", "").upper()
+
         url = "https://data.alpaca.markets/v2/stocks/quotes/latest"
 
         params = {"symbols": symbol, "feed": feed, "currency": currency}
@@ -30,16 +41,16 @@ def get(
 
         for key, value in response["quotes"].items():
             quotes.append(
-                {
-                    "symbol": key,
-                    "timestamp": pendulum.parse(
-                        value["t"], tz="America/New_York"
-                    ).to_datetime_string(),
-                    "ask": value["ap"],
-                    "ask_size": value["as"],
-                    "bid": value["bp"],
-                    "bid_size": value["bs"],
-                }
+                quote_class_from_dict(
+                    {
+                        "symbol": key,
+                        "timestamp": value["t"],
+                        "ask": value["ap"],
+                        "ask_size": value["as"],
+                        "bid": value["bp"],
+                        "bid_size": value["bs"],
+                    }
+                )
             )
 
-        print(quotes)
+        return quotes if len(quotes) > 1 else quotes[0]

src/py_alpaca_api/stock/latest_quote.py

@@ -3,35 +3,10 @@
 import pandas as pd
 import pendulum
 from prophet import Prophet
-# from tqdm import tqdm
 
 from py_alpaca_api.stock.history import History
 from py_alpaca_api.stock.screener import Screener
 
-from rich.console import Console
-
-from rich.progress import (
-    BarColumn,
-    MofNCompleteColumn,
-    Progress,
-    TextColumn,
-    TimeElapsedColumn,
-    TimeRemainingColumn,
-)
-
-# Define custom progress bar
-progress_bar = Progress(
-    TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
-    BarColumn(),
-    MofNCompleteColumn(),
-    TextColumn("•"),
-    TimeElapsedColumn(),
-    TextColumn("•"),
-    TimeRemainingColumn(),
-)
-
-console = Console()
-
 yesterday = pendulum.now().subtract(days=1).format("YYYY-MM-DD")
 four_years_ago = pendulum.now().subtract(years=2).format("YYYY-MM-DD")
 
@@ -147,37 +122,23 @@ def get_losers_to_gainers(
 
         future_gainers = []
 
-        with progress_bar as progress:
-            # for i, ticker in tqdm(
-            #     enumerate(losers_list),
-            #     desc=f"• Predicting {len(losers_list)} future gainers with Prophet: ",
-            # ):
-            console.print(
-                f"Getting predictions for [bold]{len(losers_list)}[/bold] future gainers with Prophet: ",
-                style="green",
-            )
-            for i, ticker in progress.track(
-                enumerate(losers_list), total=len(losers_list)
-            ):
-                try:
-                    symbol_data = self.get_stock_data(ticker)
-                    symbol_model = self.train_prophet_model(symbol_data)
-                    symbol_forecast = self.generate_forecast(
-                        symbol_model, future_periods=future_periods
-                    )
-                    previous_price = previous_day_losers[
-                        previous_day_losers["symbol"] == ticker
-                    ].iloc[0]["price"]
-                    gain_prediction = round(
-                        ((symbol_forecast - previous_price) / previous_price) * 100, 2
-                    )
-                    if gain_prediction >= gain_ratio:
-                        future_gainers.append(ticker)
-                except Exception as e:
-                    logger.error(f"Error predicting {ticker}: {e}")
-                    continue
-        console.print(
-            f"Predicted [bold]{len(future_gainers)}[/bold] future gainers.",
-            style="yellow",
-        )
+        for i, ticker in enumerate(losers_list):
+            try:
+                symbol_data = self.get_stock_data(ticker)
+                symbol_model = self.train_prophet_model(symbol_data)
+                symbol_forecast = self.generate_forecast(
+                    symbol_model, future_periods=future_periods
+                )
+                previous_price = previous_day_losers[
+                    previous_day_losers["symbol"] == ticker
+                ].iloc[0]["price"]
+                gain_prediction = round(
+                    ((symbol_forecast - previous_price) / previous_price) * 100, 2
+                )
+                if gain_prediction >= gain_ratio:
+                    future_gainers.append(ticker)
+            except Exception as e:
+                logger.error(f"Error predicting {ticker}: {e}")
+                continue
+        print(f"Predicted [bold]{len(future_gainers)}[/bold] future gainers.")
         return future_gainers