update
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MuslemRahimi
parent
4a8500e8ca
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7a2925a46a
@ -88,9 +88,12 @@ def replace_representative(office):
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'Vance, J.D. (Senator)': 'James Vance',
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'Neal Patrick MD, Facs Dunn': 'Neal Dunn',
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'Neal Patrick MD, Facs Dunn (Senator)': 'Neal Dunn',
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'Neal Patrick Dunn, MD, FACS': 'Neal Dunn',
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'Neal P. Dunn': 'Neal Dunn',
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'Tillis, Thom (Senator)': 'Thom Tillis',
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'W. Gregory Steube': 'Greg Steube',
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'W. Grego Steube': 'Greg Steube',
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'W. Greg Steube': 'Greg Steube',
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'David David Madison Cawthorn': 'David Madison Cawthorn',
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'Blunt, Roy (Senator)': 'Roy Blunt',
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'Thune, John (Senator)': 'John Thune',
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@ -303,7 +306,7 @@ async def run():
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total_symbols = crypto_symbols +etf_symbols + stock_symbols
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total_raw_data = stock_raw_data + etf_raw_data + crypto_raw_data
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chunk_size = 250
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chunk_size = 500
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politician_list = []
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except Exception as e:
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@ -45,8 +45,10 @@ async def download_data(ticker, con, start_date, end_date):
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income = ujson.loads(query_df['income'].iloc[0])
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#Only consider company with at least 10 year worth of data
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'''
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if len(income) < 40:
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raise ValueError("Income data length is too small.")
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'''
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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]
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income_growth = ujson.loads(query_df['income_growth'].iloc[0])
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@ -109,11 +111,11 @@ async def download_data(ticker, con, start_date, end_date):
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combined_data = sorted(combined_data, key=lambda x: x['date'])
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df_income = pd.DataFrame(combined_data).dropna()
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df_combined = pd.DataFrame(combined_data).dropna()
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df_income['Target'] = ((df_income['price'].shift(-1) - df_income['price']) / df_income['price'] > 0).astype(int)
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df_combined['Target'] = ((df_combined['price'].shift(-1) - df_combined['price']) / df_combined['price'] > 0).astype(int)
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df_copy = df_income.copy()
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df_copy = df_combined.copy()
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return df_copy
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@ -208,7 +210,7 @@ class FundamentalPredictor:
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return {'accuracy': round(test_accuracy*100), 'precision': round(test_precision*100), 'sentiment': 'Bullish' if next_value_prediction == 1 else 'Bearish'}, test_predictions
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def feature_selection(self, X_train, y_train,k=8):
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'''
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selector = SelectKBest(score_func=f_classif, k=8)
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selector.fit(X_train, y_train)
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@ -216,8 +218,9 @@ class FundamentalPredictor:
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selected_features = [col for i, col in enumerate(X_train.columns) if selector.get_support()[i]]
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return selected_features
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'''
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# Calculate the variance of each feature with respect to the target
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'''
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variances = {}
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for col in X_train.columns:
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grouped_variance = X_train.groupby(y_train)[col].var().mean()
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@ -226,6 +229,7 @@ class FundamentalPredictor:
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# Sort features by variance and select top k features
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sorted_features = sorted(variances, key=variances.get, reverse=True)[:k]
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return sorted_features
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'''
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#Train mode
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async def train_process(tickers, con):
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@ -272,7 +276,7 @@ async def test_process(con):
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start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
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end_date = datetime.today().strftime("%Y-%m-%d")
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predictor = FundamentalPredictor()
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df = await download_data('GME', con, start_date, end_date)
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df = await download_data('RDDT', con, start_date, end_date)
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split_size = int(len(df) * (1-test_size))
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test_data = df.iloc[split_size:]
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#selected_features = [col for col in test_data if col not in ['price','date','Target']]
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@ -283,8 +287,9 @@ async def main():
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con = sqlite3.connect('../stocks.db')
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cursor = con.cursor()
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cursor.execute("PRAGMA journal_mode = wal")
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 500E9")
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 100E9")
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stock_symbols = [row[0] for row in cursor.fetchall()]
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print('Number of Stocks')
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print(len(stock_symbols))
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await train_process(stock_symbols, con)
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await test_process(con)
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@ -491,6 +491,7 @@ async def get_delisted_list():
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def replace_representative(office):
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replacements = {
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'Carper, Thomas R. (Senator)': 'Tom Carper',
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@ -541,9 +542,12 @@ def replace_representative(office):
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'Vance, J.D. (Senator)': 'James Vance',
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'Neal Patrick MD, Facs Dunn': 'Neal Dunn',
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'Neal Patrick MD, Facs Dunn (Senator)': 'Neal Dunn',
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'Neal Patrick Dunn, MD, FACS': 'Neal Dunn',
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'Neal P. Dunn': 'Neal Dunn',
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'Tillis, Thom (Senator)': 'Thom Tillis',
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'W. Gregory Steube': 'Greg Steube',
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'W. Grego Steube': 'Greg Steube',
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'W. Greg Steube': 'Greg Steube',
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'David David Madison Cawthorn': 'David Madison Cawthorn',
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'Blunt, Roy (Senator)': 'Roy Blunt',
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'Thune, John (Senator)': 'John Thune',
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@ -78,8 +78,8 @@ module.exports = function (fastify, opts, done) {
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filter += `&& created >= "${startDateStr}" && created <= "${endDateStr}" && pinned = false`
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}
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posts = (await pb.collection('posts').getList(data?.startPage, 50, {
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sort: sort,
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posts = (await pb.collection('posts').getList(data?.startPage, 5, {
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sort: '-created',
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filter: filter,
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expand: 'user,comments(post),alreadyVoted(post)',
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fields: "*,expand.user,expand.comments(post), expand.alreadyVoted(post).user,expand.alreadyVoted(post).type"
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@ -99,7 +99,7 @@ module.exports = function (fastify, opts, done) {
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if (data?.userId) {
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posts = (await pb.collection('posts').getList(data?.startPage, 10, {
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posts = (await pb.collection('posts').getList(data?.startPage, 5, {
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sort: sort,
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filter: `user="${data?.userId}" && pinned=false`,
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expand: `user,comments(post),alreadyVoted(post)`,
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@ -111,7 +111,7 @@ module.exports = function (fastify, opts, done) {
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else if (data?.filterTicker) {
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posts = await pb.collection('posts').getList(data?.startPage, 10, {
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posts = await pb.collection('posts').getList(data?.startPage, 5, {
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sort: sort,
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filter: `tagline="${data?.filterTicker}" && pinned=false`,
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expand: `user,comments(post),alreadyVoted(post)`,
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@ -141,8 +141,8 @@ module.exports = function (fastify, opts, done) {
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else {
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filter = `pinned=false`;
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}
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posts = await pb.collection('posts').getList(data?.startPage, 50, {
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sort: sort,
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posts = await pb.collection('posts').getList(1, 5, {
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sort: '-created',
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filter: filter,
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expand: 'user, comments(post), alreadyVoted(post)',
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fields: "*,expand.user,expand.comments(post), expand.alreadyVoted(post).user,expand.alreadyVoted(post).type"
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