update

app/cron_congress_trading.py

@@ -88,9 +88,12 @@ def replace_representative(office):
         'Vance, J.D. (Senator)': 'James Vance',
         'Neal Patrick MD, Facs Dunn': 'Neal Dunn',
         'Neal Patrick MD, Facs Dunn (Senator)': 'Neal Dunn',
+        'Neal Patrick Dunn, MD, FACS': 'Neal Dunn',
+        'Neal P. Dunn': 'Neal Dunn',
         'Tillis, Thom (Senator)': 'Thom Tillis',
         'W. Gregory Steube': 'Greg Steube',
         'W. Grego Steube': 'Greg Steube',
+        'W. Greg Steube': 'Greg Steube',
         'David David Madison Cawthorn': 'David Madison Cawthorn',
         'Blunt, Roy (Senator)': 'Roy Blunt',
         'Thune, John (Senator)': 'John Thune',
@@ -303,7 +306,7 @@ async def run():
 
         total_symbols = crypto_symbols +etf_symbols + stock_symbols
         total_raw_data = stock_raw_data + etf_raw_data + crypto_raw_data
-        chunk_size = 250
+        chunk_size = 500
         politician_list = []
 
     except Exception as e:

app/ml_models/fundamental_predictor.py

@@ -45,8 +45,10 @@ async def download_data(ticker, con, start_date, end_date):
         income =  ujson.loads(query_df['income'].iloc[0])
 
         #Only consider company with at least 10 year worth of data
+        '''
         if len(income) < 40:
             raise ValueError("Income data length is too small.")
+        '''
 
         income = [{k: v for k, v in item.items() if k not in ["symbol","reportedCurrency","calendarYear","fillingDate","acceptedDate","period","cik","link", "finalLink"]} for item in income if int(item["date"][:4]) >= 2000]
         income_growth = ujson.loads(query_df['income_growth'].iloc[0])
@@ -109,11 +111,11 @@ async def download_data(ticker, con, start_date, end_date):
         combined_data = sorted(combined_data, key=lambda x: x['date'])
 
         
-        df_income = pd.DataFrame(combined_data).dropna()
+        df_combined = pd.DataFrame(combined_data).dropna()
 
-        df_income['Target'] = ((df_income['price'].shift(-1) - df_income['price']) / df_income['price'] > 0).astype(int)
+        df_combined['Target'] = ((df_combined['price'].shift(-1) - df_combined['price']) / df_combined['price'] > 0).astype(int)
 
-        df_copy = df_income.copy()
+        df_copy = df_combined.copy()
         
         return df_copy
 
@@ -208,7 +210,7 @@ class FundamentalPredictor:
         return {'accuracy': round(test_accuracy*100), 'precision': round(test_precision*100), 'sentiment': 'Bullish' if next_value_prediction == 1 else 'Bearish'}, test_predictions
 
     def feature_selection(self, X_train, y_train,k=8):
-        '''
+        
         selector = SelectKBest(score_func=f_classif, k=8)
         selector.fit(X_train, y_train)
 
@@ -216,8 +218,9 @@ class FundamentalPredictor:
         selected_features = [col for i, col in enumerate(X_train.columns) if selector.get_support()[i]]
 
         return selected_features
-        '''
+        
         # Calculate the variance of each feature with respect to the target
+        '''
         variances = {}
         for col in X_train.columns:
             grouped_variance = X_train.groupby(y_train)[col].var().mean()
@@ -226,6 +229,7 @@ class FundamentalPredictor:
         # Sort features by variance and select top k features
         sorted_features = sorted(variances, key=variances.get, reverse=True)[:k]
         return sorted_features
+        '''
 
 #Train mode
 async def train_process(tickers, con):
@@ -272,7 +276,7 @@ async def test_process(con):
     start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
     end_date = datetime.today().strftime("%Y-%m-%d")
     predictor = FundamentalPredictor()
-    df = await download_data('GME', con, start_date, end_date)
+    df = await download_data('RDDT', con, start_date, end_date)
     split_size = int(len(df) * (1-test_size))
     test_data = df.iloc[split_size:]
     #selected_features = [col for col in test_data if col not in ['price','date','Target']]
@@ -283,8 +287,9 @@ async def main():
     con = sqlite3.connect('../stocks.db')
     cursor = con.cursor()
     cursor.execute("PRAGMA journal_mode = wal")
-    cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 500E9")
+    cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 100E9")
     stock_symbols = [row[0] for row in cursor.fetchall()]
+    print('Number of Stocks')
     print(len(stock_symbols))
     await train_process(stock_symbols, con)
     await test_process(con)

app/restart_json.py

@@ -491,6 +491,7 @@ async def get_delisted_list():
 
 
 
+
 def replace_representative(office):
     replacements = {
         'Carper, Thomas R. (Senator)': 'Tom Carper',
@@ -541,9 +542,12 @@ def replace_representative(office):
         'Vance, J.D. (Senator)': 'James Vance',
         'Neal Patrick MD, Facs Dunn': 'Neal Dunn',
         'Neal Patrick MD, Facs Dunn (Senator)': 'Neal Dunn',
+        'Neal Patrick Dunn, MD, FACS': 'Neal Dunn',
+        'Neal P. Dunn': 'Neal Dunn',
         'Tillis, Thom (Senator)': 'Thom Tillis',
         'W. Gregory Steube': 'Greg Steube',
         'W. Grego Steube': 'Greg Steube',
+        'W. Greg Steube': 'Greg Steube',
         'David David Madison Cawthorn': 'David Madison Cawthorn',
         'Blunt, Roy (Senator)': 'Roy Blunt',
         'Thune, John (Senator)': 'John Thune',

fastify/get-post/server.js

@@ -78,8 +78,8 @@ module.exports = function (fastify, opts, done) {
                 filter += `&& created >= "${startDateStr}" && created <= "${endDateStr}" && pinned = false`
             }
 
-            posts = (await pb.collection('posts').getList(data?.startPage, 50, {
+            posts = (await pb.collection('posts').getList(data?.startPage, 5, {
-                sort: sort,
+                sort: '-created',
                 filter: filter,
                 expand: 'user,comments(post),alreadyVoted(post)',
                 fields: "*,expand.user,expand.comments(post), expand.alreadyVoted(post).user,expand.alreadyVoted(post).type"
@@ -99,7 +99,7 @@ module.exports = function (fastify, opts, done) {
 
             if (data?.userId) {
             
-            posts = (await pb.collection('posts').getList(data?.startPage, 10, {
+            posts = (await pb.collection('posts').getList(data?.startPage, 5, {
                 sort: sort,
                 filter: `user="${data?.userId}" && pinned=false`,
                 expand: `user,comments(post),alreadyVoted(post)`,
@@ -111,7 +111,7 @@ module.exports = function (fastify, opts, done) {
 
             else if (data?.filterTicker) {
             
-                posts = await pb.collection('posts').getList(data?.startPage, 10, {
+                posts = await pb.collection('posts').getList(data?.startPage, 5, {
                     sort: sort,
                     filter: `tagline="${data?.filterTicker}" && pinned=false`,
                     expand: `user,comments(post),alreadyVoted(post)`,
@@ -141,8 +141,8 @@ module.exports = function (fastify, opts, done) {
                 else {
                     filter = `pinned=false`;
                 }
-                posts = await pb.collection('posts').getList(data?.startPage, 50, {
+                posts = await pb.collection('posts').getList(1, 5, {
-                    sort: sort,
+                    sort: '-created',
                     filter: filter,
                     expand: 'user, comments(post), alreadyVoted(post)',
                     fields: "*,expand.user,expand.comments(post), expand.alreadyVoted(post).user,expand.alreadyVoted(post).type"