First simple case prediction (no training, just return prediction based on last day return)

appunti.md

@@ -0,0 +1,10 @@
+# Appunti sullo sviluppo del progetto
+
+Il titolo di riferimento per la prima parte di studio è Microsoft. Perché:
+* molto capitalizzato
+* longevo
+* è un titolo tech ma non subisce dinamiche troppo "strane" rispetto al normale andamento di mercato (es. Tesla)
+
+Per prima cosa si testa la performance di un modello non trainato, semplice, che prova a predire il segno del ritorno del giorno successivo sulla base del ritorno del giorno precedente (+ segue + e - segue -).
+
+Si riporta anche un piccolo grafico a barre per avere un'idea della distribuzione dei ritorni.

data/.~lock.MSFT_data.csv#

@@ -0,0 +1 @@
+,master-roby3,master-roby3,27.10.2023 01:59,file:///home/master-roby3/.config/libreoffice/4;

data/.~lock.daily_MSFT.csv#

@@ -1 +0,0 @@
-,master-roby3,master-roby3,26.10.2023 16:32,file:///home/master-roby3/.config/libreoffice/4;

main.py

@@ -1,5 +0,0 @@
-import pandas as pd
-
-stock_data = pd.read_csv("data/daily_MSFT.csv", index_col="timestamp")
-
-print(stock_data)

simple_sign_prediction.py

@@ -0,0 +1,92 @@
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+import yfinance as yf
+from datetime import datetime
+
+import os
+
+#bodacious colors
+colors=sns.color_palette("rocket", 8)
+#Ram's colors, if desired
+seshadri = ['#c3121e', '#0348a1', '#ffb01c', '#027608', '#0193b0', '#9c5300', '#949c01', '#7104b5']
+#            0sangre,   1neptune,  2pumpkin,  3clover,   4denim,    5cocoa,    6cumin,    7berry
+
+#stock_data = pd.read_csv("data/daily_MSFT.csv").iloc[::-1].reset_index(drop=True)
+
+if os.path.isfile("data/MSFT_data.pkl"):
+    stock_data = pd.read_pickle("data/MSFT_data.pkl")
+elif os.path.isfile("data/MSFT_data.csv"):
+    stock_data = pd.read_csv("data/MSFT_data.csv")
+else:
+    start_date = datetime(2000, 1, 1)
+    end_date = datetime(2023, 10, 26)
+    stock_data = yf.download('MSFT', start=start_date, end=end_date)
+    stock_data.to_pickle("data/MSFT_data.pkl")
+    stock_data.to_csv("data/MSFT_data.csv")
+
+
+
+
+daily_returns = stock_data["Close"] - stock_data["Open"]
+win_lose = np.zeros(daily_returns.size - 1)
+
+for index, return_ in enumerate(daily_returns[:-1]):
+    if (return_ > 0 and daily_returns[index + 1] > 0) or (return_ < 0 and daily_returns[index + 1] < 0):
+        win_lose[index] = 1
+    else:
+        win_lose[index] = 0
+
+
+win_rate = np.count_nonzero(win_lose == 1) / win_lose.size
+
+print(win_rate)
+
+percent_returns = daily_returns / stock_data["Open"] * 100
+
+print([ret for ret in percent_returns if ret > 10 or ret < -10])
+
+fig = plt.figure(1, figsize=(15,10))
+plt.hist(percent_returns, bins = 120, range=(-12,12), facecolor=seshadri[0], alpha=0.8, edgecolor="white", label="Percentage daily returns occurrences")
+
+#plt.plot(stock_data.index, stock_data["Close"], linestyle="-", color=seshadri[0])
+#plt.plot(stock_data.index, stock_data["Adj Close"], linestyle="-", color=seshadri[1])
+#plt.plot(stock_data.index, stock_data["close"] - 20, linestyle="-", color=seshadri[2])
+#plt.plot(stock_data.index, stock_data["close"] - 30, linestyle="-", color=seshadri[3])
+#plt.plot(stock_data.index, stock_data["close"] - 40, linestyle="-", color=seshadri[4])
+#plt.plot(stock_data.index, stock_data["close"] - 50, linestyle="-", color=seshadri[5])
+#plt.plot(stock_data.index, stock_data["close"] - 60, linestyle="-", color=seshadri[6])
+#plt.plot(stock_data.index, stock_data["close"] - 70, linestyle="-", color=seshadri[7])
+#plt.show()
+
+
+#plot params
+plt.xlim([-12,12])
+#plt.ylim([-0.5,16])
+plt.minorticks_on()
+plt.tick_params(labelsize=14)
+plt.tick_params(labelbottom=True, labeltop=False, labelright=False, labelleft=True)
+#xticks = np.arange(0, 1e4,10)
+#yticks = np.arange(0,16.1,4)
+
+plt.tick_params(direction='in',which='minor', length=5, bottom=True, top=True, left=True, right=True)
+plt.tick_params(direction='in',which='major', length=10, bottom=True, top=True, left=True, right=True)
+#plt.xticks(xticks)
+#plt.yticks(yticks)
+
+
+#plt.text(1,325, f'y={Decimal(coefs[3]):.4f}x$^3$+{Decimal(coefs[2]):.2f}x$^2$+{Decimal(coefs[1]):.2f}x+{Decimal(coefs[0]):.1f}',fontsize =13)
+
+
+plt.xlabel(r'Percentage daily return', fontsize=14) 
+plt.ylabel(r'Occurrences',fontsize=14)  # label the y axis
+
+
+plt.legend(fontsize=14, loc="upper right", bbox_to_anchor=(0.99, 0.99))  # add the legend (will default to 'best' location)
+
+plt.savefig("plots/MSFT_daily_occs.png", dpi=300)
+
+plt.show()
+