Start with optimize something exercise. Also add a playground for testing candlestick plotting via mplfinance.

playground/play.py

@@ -0,0 +1,36 @@
+import os
+import pandas as pd
+import datetime as dt
+
+
+def symbol_to_path(symbol, base_dir=None):
+    """Return CSV file path given ticker symbol."""
+    if base_dir is None:
+        base_dir = os.environ.get("MARKET_DATA_DIR", '../data/')
+    return os.path.join(base_dir, "{}.csv".format(str(symbol)))
+
+def get_data(path, dates):
+    """Read stock data (adjusted close) for given symbols from CSV files."""
+    df = pd.DataFrame(index=dates)
+    df_temp = pd.read_csv(path,
+            index_col='time',
+            parse_dates=True,
+            usecols=['time', 'open', 'high', 'low', 'close'],
+            na_values=['nan'])
+    df = df.join(df_temp)
+    return df
+
+def plot_data(df, title="Stock prices", xlabel="Date", ylabel="Price"):
+    import mplfinance as mpf
+    mpf.plot(df, type='candle', mav=(9, 24))
+
+def test_code():
+    sd = dt.datetime(2020,1,1)
+    ed = dt.datetime(2020,8,30)
+    dates = pd.date_range(sd, ed)
+    prices_all = get_data(symbol_to_path('BTCUSD_Coinbase'), dates)
+    plot_data(prices_all)
+
+if __name__ == "__main__":
+    test_code()
+