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