Initial commit

stockdash_main.py

@@ -0,0 +1,115 @@
+import os
+import pandas as pd
+import json
+from datetime import datetime
+import time
+
+import stockdash_render
+import yfinance as yf
+
+API_KEY = '44ced5e44c50543745b1d89fce8cd93a'
+api_key = "?apikey=" + API_KEY
+api_kpi_url = "https://financialmodelingprep.com/api/v3/key-metrics/"
+api_batch_stock_price_url = "https://financialmodelingprep.com/api/v3/quote/"
+
+data_dir = 'data'
+file_symbol = os.path.join(data_dir, 'symbols.json')
+file_kpi = os.path.join(data_dir, 'comp_kpi.csv')
+file_div = os.path.join(data_dir, 'comp_div.csv')
+file_rec = os.path.join(data_dir, 'comp_rec.csv')
+file_his = os.path.join(data_dir, 'comp_his.csv')
+
+sym_exclude = []
+used_columns = ['symbol', 'shortName', 'sector', 'industry', 'country', 'marketCap', 'enterpriseValue', 'dividendRate',
+                'trailingPE', 'forwardPE', 'enterpriseToEbitda', 'shortRatio']
+
+
+def load_symbols():
+    symbols = []
+    with open(file_symbol) as json_file:
+        data = json.load(json_file)
+    for sym in data:
+        if 'exchange' in sym and sym['exchange'] == 'Nasdaq Global Select' and sym['symbol'] not in sym_exclude:
+            symbols.append(sym['symbol'])
+    return symbols
+
+
+def get_data(symbols):
+    tickers = yf.Tickers(' '.join(symbols))
+
+    if os.path.exists(file_kpi) and os.path.exists(file_div) and os.path.exists(file_rec) and os.path.exists(file_his):
+        print("Found cached files. Loading cache...")
+        kpi_data = pd.read_csv(file_kpi)
+        print("%s KPIs loaded..." % len(kpi_data))
+        div_data = pd.read_csv(file_div)
+        print("%s Dividends loaded..." % len(div_data))
+        rec_data = pd.read_csv(file_rec)
+        print("%s Recommendations loaded..." % len(rec_data))
+        his_data = pd.read_csv(file_his)
+        print("%s Price History loaded..." % len(his_data))
+    else:
+        kpi_data, div_data, rec_data, his_data = pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
+        i = 1
+        for ticker in tickers.tickers:
+            try:
+                info = ticker.info
+                print("%s/%s Querying data for ticker %s" % (i, len(symbols), info['symbol']))
+                kpi_data = kpi_data.append(info, ignore_index=True)
+
+                div = ticker.dividends.to_frame().reset_index()
+                div.insert(0, 'Symbol', info['symbol'])
+                div_data = div_data.append(div, ignore_index=True)
+
+                rec = ticker.recommendations.reset_index()
+                rec.insert(0, 'Symbol', info['symbol'])
+                rec_data = rec_data.append(rec, ignore_index=True)
+
+                his = ticker.history(period='5y').reset_index()
+                his.insert(0, 'Symbol', info['symbol'])
+                his_data = his_data.append(his, ignore_index=True)
+                i += 1
+            except Exception:
+                print("Error occured when quering %s - skipping this entry")
+                continue
+
+        kpi_data = kpi_data[['symbol'] + [col for col in kpi_data.columns if col != 'symbol']]
+
+        rec_data['Date'] = pd.to_datetime(rec_data['Date'])
+
+        his_data['priceMA50'] = his_data['Close'].rolling(window=50).mean()
+        his_data['priceMA200'] = his_data['Close'].rolling(window=200).mean()
+        his_data['diffMA50_200'] = his_data['priceMA50'] - his_data['priceMA200']
+
+        kpi_data.to_csv(file_kpi)
+        div_data.to_csv(file_div)
+        rec_data.to_csv(file_rec)
+        his_data.to_csv(file_his)
+
+    return kpi_data, div_data, rec_data, his_data
+
+
+if __name__ == '__main__':
+    start_time = time.time()
+    print("----- Starting STOCKDASH @ %s -----" % datetime.fromtimestamp(start_time))
+    used_symbols = load_symbols()
+
+    print("%s symbols loaded from file" % len(used_symbols))
+    kpi_data, div_data, rec_data, his_data = get_data(used_symbols[:100])
+
+    # Modify Recommendation Data
+    rec_data_mod = pd.concat([rec_data, pd.get_dummies(rec_data['To Grade'], prefix='grade')], axis=1)
+    rec_data_mod.drop(['To Grade', 'From Grade', 'Action'], axis=1, inplace=True)
+    rec_data_mod['Date'] = pd.to_datetime(rec_data_mod['Date'])
+    df2 = rec_data_mod.groupby([pd.Grouper(key='Date', freq='Y'), pd.Grouper('Symbol')]).agg(['sum'])
+
+    df2['Positive'] = df2['grade_Buy'] + df2['grade_Outperform'] + df2['grade_Market Outperform'] + df2[
+        'grade_Overweight'] + df2['grade_Positive'] + df2['grade_Strong Buy']
+    df2['Neutral'] = df2['grade_Equal-Weight'] + df2['grade_Hold'] + df2['grade_Neutral']
+    df2['Negative'] = df2['grade_Market Underperform'] + df2['grade_Reduce'] + df2['grade_Sell'] + df2[
+        'grade_Underweight']
+
+    columns = ['Positive', 'Neutral', 'Negative']
+    rec_data_mod = df2[columns]
+
+    print("Data loaded after %ss" % (time.time()-start_time))
+    stockdash_render.load_dash(kpi_data[used_columns], rec_data_mod, div_data, his_data)