Add SMA to prediction techniques menu

prediction_techniques/pred_menu.py

@@ -0,0 +1,74 @@
+import config_bot as cfg
+import argparse
+import datetime
+from datetime import datetime
+from stock_market_helper_funcs import *
+import config_bot as cfg
+import pandas as pd
+import matplotlib.pyplot as plt
+from pandas.plotting import register_matplotlib_converters
+register_matplotlib_converters()
+
+from prediction_techniques import sma
+
+# -----------------------------------------------------------------------------------------------------------------------
+def print_prediction(s_ticker, s_start, s_interval):
+    """ Print help """
+
+    s_intraday = (f'Intraday {s_interval}', 'Daily')[s_interval == "1440min"]
+
+    if s_start:
+        print(f"\n{s_intraday} Stock: {s_ticker} (from {s_start.strftime('%Y-%m-%d')})")
+    else:
+        print(f"\n{s_intraday} Stock: {s_ticker}")
+
+    print("\nPrediction Techniques:")
+    print("   help        show this prediction techniques menu again")
+    print("   q           quit this menu, and shows back to main menu")
+    print("   quit        quit to abandon program")
+    print("")
+    print("   sma         simple moving average")
+    print("")
+ 
+
+# ---------------------------------------------------- MENU ----------------------------------------------------
+def pred_menu(df_stock, s_ticker, s_start, s_interval):
+
+    # Add list of arguments that the prediction techniques parser accepts
+    pred_parser = argparse.ArgumentParser(prog='technical_analysis', add_help=False)
+    pred_parser.add_argument('cmd', choices=['help', 'q', 'quit',
+                                             'sma'])
+
+    print_prediction(s_ticker, s_start, s_interval)
+
+    # Loop forever and ever
+    while True:
+        # Get input command from user
+        as_input = input('> ')
+        
+        # Parse prediction techniques command of the list of possible commands
+        try:
+            (ns_known_args, l_args) = pred_parser.parse_known_args(as_input.split())
+
+        except SystemExit:
+            print("The command selected doesn't exist\n")
+            continue
+
+        if ns_known_args.cmd == 'help':
+            print_prediction(s_ticker, s_start, s_interval)
+
+        elif ns_known_args.cmd == 'q':
+            # Just leave the FA menu
+            return False
+
+        elif ns_known_args.cmd == 'quit':
+            # Abandon the program
+            return True
+
+        # ------------------------------------------ SIMPLE MOVING AVERAGE ------------------------------------------
+        elif ns_known_args.cmd == 'sma':
+            sma.simple_moving_average(l_args, s_ticker, s_interval, df_stock)
+
+        # ------------------------------------------------------------------------------------------------------------
+        else:
+            print("Command not recognized!")

prediction_techniques/sma.py

@@ -0,0 +1,64 @@
+import argparse
+import numpy as np
+from stock_market_helper_funcs import *
+import pandas as pd
+import matplotlib.pyplot as plt
+from pandas.plotting import register_matplotlib_converters
+register_matplotlib_converters()
+
+# ----------------------------------------------------- SMA -----------------------------------------------------
+def simple_moving_average(l_args, s_ticker, s_interval, df_stock):
+    parser = argparse.ArgumentParser(prog='simple_moving_average',
+                                     description=""" Moving Averages are used to smooth the data in an array to 
+                                     help eliminate noise and identify trends. The Simple Moving Average is literally 
+                                     the simplest form of a moving average. Each output value is the average of the 
+                                     previous n values. In a Simple Moving Average, each value in the time period carries 
+                                     equal weight, and values outside of the time period are not included in the average. 
+                                     This makes it less responsive to recent changes in the data, which can be useful for 
+                                     filtering out those changes. """)
+
+    parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=20, help='length of SMA window')
+    parser.add_argument('-d', "--days", action="store", dest="n_days", type=check_positive, default=5, help='prediction days')
+
+    try:
+        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
+
+        if l_unknown_args:
+            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
+            return
+
+        l_predictions = list()
+        for pred_day in range(ns_parser.n_days):
+            if pred_day < ns_parser.n_length:
+                l_ma_stock = df_stock['4. close'].values[-ns_parser.n_length+pred_day:]
+            else:
+                l_ma_stock = list()
+            l_predictions.append(np.mean(np.append(l_ma_stock, l_predictions)))
+
+        l_pred_days = get_next_stock_market_days(last_stock_day=df_stock['4. close'].index[-1], n_next_days=ns_parser.n_days)
+        df_pred = pd.Series(l_predictions, index=l_pred_days, name='Price') 
+
+        plt.plot(df_stock.index, df_stock['4. close'], lw=3)
+        plt.title(f"{ns_parser.n_length} Moving Average on {s_ticker} - {ns_parser.n_days} days prediction")
+        plt.xlim(df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 10)[-1])
+        plt.xlabel('Time')
+        plt.ylabel('Share Price ($)')
+        plt.grid(b=True, which='major', color='#666666', linestyle='-')
+        plt.minorticks_on()
+        plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
+        df_ma = df_stock['4. close'].rolling(window=ns_parser.n_length).mean()
+        plt.plot(df_ma.index, df_ma, lw=2)
+        plt.plot(df_pred.index, df_pred, lw=2)
+        cmap = plt.get_cmap("tab10")
+        plt.axvspan(df_pred.index[0], df_pred.index[-1], facecolor=cmap(1), alpha=0.2)
+        xmin, xmax, ymin, ymax = plt.axis()
+        plt.vlines(df_stock.index[-1], ymin, ymax, colors=cmap(0), linewidth=1, linestyle='--', color='k')
+        plt.show()
+
+        df_pred = df_pred.apply(lambda x: f"{x:.2f} $")
+        print(df_pred.to_string())
+        print("")
+
+    except:
+        print("")
+

stock_market_cli.py

@@ -1,42 +1,7 @@
 """ Example or something
 
-    Is this not informative enough?
-
-    Load a specific stock
-    Then apply a specific TA on it
-
-    Do this because AlphaVantage only allows 5 API calls per minutes.
-    This way we only need 1 call, and can apply TA to the result without
-    using an API request.
-
-    Multiple TA with kind stock
-
-    Do my own personal Fundamental Analysis Dataframe
-    Do option to explain important key metrics
-
-    Do own personal Technical Analysis with multiple indicators perhaps?
-
-    Split Menu into fa, ta and pred. Fundamental Analysis, Technical Analysis and Prediction, respectively.
-
-    Do Reddit popularity study! 
-    - By using defined ticker
-    - By looking for mentioned tickers
-    - By giving your own subreddit, using used ones.
-
-    At the end, provide save tool where we save all data into an excel with:
-
-    NIO_datetimesaved.xlsx
-    - Sheet1 - Profile/Overview (FMP or AV)
-    - Sheet2 - Finance (Market watch)
-    - Sheet3 - Earnings (Market watch)
-    - Sheet4 - SEC filings
-    - Sheet5 - Insiders
-    - Sheet6 - Analyst prices
-    - Sheet7 - News
-    - Sheet8 - Warnings & Key metrics
-    - Sheet9 - Reddit popularity
-
 """
+
 import argparse
 import pandas as pd
 from stock_market_helper_funcs import *
@@ -45,6 +10,7 @@ from technical_analysis import ta_menu as tam
 from due_diligence import dd_menu as ddm
 from discovery import disc_menu as dm
 from sentiment import sen_menu as sm
+from prediction_techniques import pred_menu as pm
 import res_menu as rm
 
 # delete this important when automatic loading tesla
@@ -205,8 +171,7 @@ def print_help(s_ticker, s_start, s_interval, b_is_market_open):
         print("   fa          fundamental analysis,    \t e.g.: income, balance, cash, earnings")
         print("   ta          technical analysis,      \t e.g.: ema, macd, rsi, adx, bbands, obv")
         print("   dd          in-depth due-diligence,  \t e.g.: news, analyst, shorts, insider, sec")
-        #print("")
-        #print("   pred        prediction techniques,   \t e.g.: regression, arima, rnn, lstm, prophet")
+        print("   pred        prediction techniques,   \t e.g.: regression, arima, rnn, lstm, prophet")
 
     '''
         print("\nPrediction:")
@@ -244,7 +209,7 @@ def main():
     menu_parser = argparse.ArgumentParser(prog='stock_market_bot', add_help=False)
     menu_parser.add_argument('opt', choices=['help', 'quit', 'q',
                                              'clear', 'load', 'view',
-                                             'disc', 'sen', 'res', 'fa', 'ta', 'dd'])
+                                             'disc', 'sen', 'res', 'fa', 'ta', 'dd', 'pred'])
                                              
 
     # Print first welcome message and help
@@ -348,6 +313,16 @@ def main():
             else:
                 print_help(s_ticker, s_start, s_interval, b_is_stock_market_open())
 
+        # PREDICTION TECHNIQUES
+        elif ns_known_args.opt == 'pred':
+            b_quit = pm.pred_menu(df_stock, s_ticker, s_start, s_interval)
+
+            if b_quit:
+                print("Hope you made money today. Good bye my lover, good bye my friend.\n")
+                return
+            else:
+                print_help(s_ticker, s_start, s_interval, b_is_stock_market_open())
+
         else:
             print('Shouldnt see this command!')