sin data

README.md

@@ -80,7 +80,8 @@ CAIS++ Spring 2019 Project: Building an Agent to Trade with Reinforcement Learni
 - Integrate NLP sentiment analysis as feature
 - Add more indicators to model
 - Clean up README
-- Do we hold positions overnight?
+- Do we hold positions overnight? I think initially no. There are also weird jumps over holidays and weekends.
+- Take into account high, low, close, volume data
 
 ## Interesting Resources
 

data/generate-sine-data.py

@@ -0,0 +1,33 @@
+# Import dependencies
+import pandas as pd
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+import random
+
+# Create a sine wave dataset in a similar format to our stock data
+# We'll use minute data from 9:30am to 4:00pm.
+def main():
+    minute_data = []
+    sines = []
+    for date in pd.date_range("01-02-2014", "01-18-2019"):
+        new_minutes = list(pd.date_range(str(date.date()) + " 9:30",
+                           str(date.date()) + " 16:00", freq="1min")
+                          )
+        minute_data += new_minutes
+
+        # Create sine data
+        x = np.arange(len(new_minutes))
+        y = np.sin(2*np.pi * random.randint(1,8) * (x/len(new_minutes)))
+
+        day_sines = y.tolist()
+        sines += day_sines
+
+    df = pd.DataFrame()
+    df['Times'] = minute_data
+    df['Sines'] = sines
+
+    df.to_csv("sin-data.csv", index=False)
+
+if __name__ == "__main__":
+    main()