feat(Data): added load_data, to aggregate everything into one dataframe

fetch_data.py

@@ -7,13 +7,30 @@ crypto_tickers = ["BTC", "ETH", "BNB", "ADA", "SOL", "XRP", "DOT", "LTC", "UNI",
 etf_tickers = ["GLD", "IEF", "TLT", "SPY", "QQQ"]
 tickers = crypto_tickers + etf_tickers
 
+add_features = False
+
 #%%
 for ticker in tickers:
     print("Fetching ", ticker)
     if ticker in crypto_tickers:
         df = get_crypto_price_crypto_compare(ticker, "USD", 1500)
     else:
-        df = get_stock_price_av(ticker, "2017-11-10")  
+        df = get_stock_price_av(ticker, "2017-11-10")
+    df['returns'] = df['close'].pct_change()
+    
+    if add_features:
+        # volatility (10, 20, 30 days)
+        df['vol_10'] = df['returns'].rolling(10).std()*(252**0.5)
+        df['vol_20'] = df['returns'].rolling(20).std()*(252**0.5)
+        df['vol_30'] = df['returns'].rolling(30).std()*(252**0.5)
+
+        # momentum (10, 20, 30, 60, 90 days)
+        df['mom_10'] = df['close'].pct_change(10)
+        df['mom_20'] = df['close'].pct_change(20)
+        df['mom_30'] = df['close'].pct_change(30)
+        df['mom_60'] = df['close'].pct_change(60)
+        df['mom_90'] = df['close'].pct_change(90)
+
     df.to_csv(f"data/{ticker}.csv", index=True)
 
 

load_data.py

@@ -0,0 +1,18 @@
+#%%
+import pandas as pd
+import os
+
+
+#%%
+
+def load_files(path):
+    dfs = [load_df(os.path.join(path,f), f.split('.')[0]) for f in os.listdir(path) if os.path.isfile(os.path.join(path,f))]
+    return pd.concat(dfs, axis=1)
+
+def load_df(path, prefix):
+    df = pd.read_csv(path, header=0, index_col=0).fillna(0)
+    df.columns = [prefix + "_" + c for c in df.columns]
+    return df
+
+load_files("data/")
+# %%