add random_state to model

app/main.py

@@ -299,6 +299,10 @@ class HeatMapData(BaseModel):
 class StockScreenerData(BaseModel):
     ruleOfList: List[str]
 
+class IndicatorListData(BaseModel):
+    ruleOfList: List[str]
+    tickerList: List[str]
+
 class TransactionId(BaseModel):
     transactionId: str
 
@@ -1097,14 +1101,38 @@ async def get_analyst_ticke_history(data: TickerData, api_key: str = Security(ge
     )
 
 
+@app.post("/indicator-data")
+async def get_indicator_data(data: IndicatorListData, api_key: str = Security(get_api_key)):
+    rule_of_list = data.ruleOfList
+    ticker_list = data.tickerList
+    always_include = ['symbol','name','price','changesPercentage']
+    try:
+        filtered_data = [
+            {key: item.get(key) for key in set(always_include+rule_of_list) if key in item}
+            for item in stock_screener_data
+            if item.get('symbol') in ticker_list  # Filter for specific tickers
+        ]
+    except Exception as e:
+        print(e)
+        filtered_data = []
+
+    # Compress the JSON data
+    res = orjson.dumps(filtered_data)
+    compressed_data = gzip.compress(res)
+
+    return StreamingResponse(
+        io.BytesIO(compressed_data),
+        media_type="application/json",
+        headers={"Content-Encoding": "gzip"}
+    )
+
+
 @app.post("/get-watchlist")
 async def get_watchlist(data: GetWatchList, api_key: str = Security(get_api_key)):
     data = data.dict()
     watchlist_id = data['watchListId']
     result = pb.collection("watchlist").get_one(watchlist_id)
     ticker_list = result.ticker
-    rule_of_list = result.rule_of_list or []
-    print(rule_of_list)
     combined_results = []  # List to store the combined results
     combined_news = []
 
@@ -1115,6 +1143,8 @@ async def get_watchlist(data: GetWatchList, api_key: str = Security(get_api_key)
         except FileNotFoundError:
             return None
 
+    quote_keys_to_include = ['volume', 'marketCap', 'changesPercentage', 'price', 'symbol', 'name']
+
     # Categorize tickers and fetch data
     for ticker in map(str.upper, ticker_list):
         ticker_type = 'stock'
@@ -1123,11 +1153,12 @@ async def get_watchlist(data: GetWatchList, api_key: str = Security(get_api_key)
         elif ticker in crypto_symbols:
             ticker_type = 'crypto'
 
-        # Load quote data
+        # Load quote data and filter to include only selected keys
         quote_dict = load_json(f"json/quote/{ticker}.json")
         if quote_dict:
-            quote_dict['type'] = ticker_type
+            filtered_quote = {key: quote_dict.get(key) for key in quote_keys_to_include}
-            combined_results.append(quote_dict)
+            filtered_quote['type'] = ticker_type  # Include ticker type
+            combined_results.append(filtered_quote)
 
         # Load news data
         news_dict = load_json(f"json/market-news/companies/{ticker}.json")
@@ -1135,12 +1166,12 @@ async def get_watchlist(data: GetWatchList, api_key: str = Security(get_api_key)
             combined_news.append(news_dict[0])
 
     # Keys to always include in the combined results
-    always_include = ['symbol', 'name', 'price', 'changesPercentage']
+    always_include = ['symbol', 'name', 'price', 'changesPercentage', 'marketCap','eps','pe','score','revenue','netIncome','freeCashFlow']
 
     try:
         # Create a mapping of stock_screener_data based on symbol for fast lookup
         screener_dict = {
-            item['symbol']: {key: item.get(key) for key in set(always_include + rule_of_list) if key in item}
+            item['symbol']: {key: item.get(key) for key in set(always_include) if key in item}
             for item in stock_screener_data
         }
 
@@ -1152,8 +1183,16 @@ async def get_watchlist(data: GetWatchList, api_key: str = Security(get_api_key)
 
     except Exception as e:
         print(f"An error occurred while merging data: {e}")
-    print(combined_results)
+
-    return [combined_results, combined_news]
+    res = {'data': combined_results, 'news': combined_news}
+    res = orjson.dumps(res)
+    compressed_data = gzip.compress(res)
+
+    return StreamingResponse(
+        io.BytesIO(compressed_data),
+        media_type="application/json",
+        headers={"Content-Encoding": "gzip"}
+    )
 
 
 def process_option_activity(item):

app/ml_models/score_model.py

@@ -24,7 +24,8 @@ class ScorePredictor:
             learning_rate=0.001,
             max_depth=10,
             num_leaves=2**10-1,
-            n_jobs=10
+            n_jobs=10,
+            random_state=42
         )
         self.warm_start_model_path = 'ml_models/weights/ai-score/stacking_weights.pkl'
         #self.pca = PCA(n_components=3)