61 lines
2.7 KiB
Python
61 lines
2.7 KiB
Python
import pandas as pd
|
|
import numpy as np
|
|
import seaborn as sns
|
|
import matplotlib.pyplot as plt
|
|
|
|
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
|
|
|
|
data = pd.read_csv("plots/data/MLP_20_10_5_2.csv", sep=";")
|
|
#data = pd.read_csv("plots/data/logistic_regression.csv", sep=";")
|
|
#data_SMA = pd.read_csv("plots/data/logistic_regression_SMA.csv", sep=";")
|
|
#data_SMA_20_50 = pd.read_csv("plots/data/logistic_regression_SMA_20_50.csv", sep=";")
|
|
#data_EMA = pd.read_csv("plots/data/logistic_regression_EMA.csv", sep=";")
|
|
#data_EMA_20_50 = pd.read_csv("plots/data/logistic_regression_EMA_20_50.csv", sep=";")
|
|
|
|
print(data)
|
|
|
|
fig = plt.figure(1, figsize=(15,10))
|
|
plt.plot(data["time_window"], data["training_accuracy"]*100, color=seshadri[0], label="Training Accuracy", linewidth=2)
|
|
plt.plot(data["time_window"], data["testing_accuracy"]*100, color=seshadri[1], label="Testing Accuracy", linewidth=2)
|
|
|
|
|
|
|
|
#plt.plot(data["time_window"], data["testing_accuracy"]*100, color=seshadri[0], label="Returns and Volume", linewidth=2)
|
|
#plt.plot(data_SMA_20_50["time_window"], data_SMA_20_50["testing_accuracy"]*100, color=seshadri[1], label="With SMA 20 and 50 candles", linewidth=2)
|
|
#plt.plot(data_SMA["time_window"], data_SMA["testing_accuracy"]*100, color=seshadri[2], label="With SMA 20, 50 and 200 candles", linewidth=2)
|
|
#plt.plot(data_EMA_20_50["time_window"], data_EMA_20_50["testing_accuracy"]*100, color=seshadri[3], label="With EMA 20 and 50 candles", linewidth=2)
|
|
#plt.plot(data_EMA["time_window"], data_EMA["testing_accuracy"]*100, color=seshadri[4], label="With EMA 20, 50 and 200 candles", linewidth=2)
|
|
|
|
|
|
#plot params
|
|
plt.xlim([0, 50])
|
|
#plt.ylim([50, 60])
|
|
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.grid(True)
|
|
|
|
#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'Lag (Days)', fontsize=14)
|
|
plt.ylabel(r'Accuracy (%)',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/MLP_20_10_5_2.png", dpi=300)
|
|
|
|
plt.show() |