def run_model(self, n, n_cluster, n_effective, step, threshold):
data = self.input_data
data = data.values.reshape(1, -1)
index = round(len(data[0]) / 3)
p1 = data[0, 0:index]
p2 = data[0, index:2 * index]
p_eval = data[0, 2 * index:]
data_pro = ProcessData(p1, n, n_cluster, n_effective)
effective = data_pro.select_effective_clusters()
test_model = Prediction(effective, p2, n, p_eval)
p = test_model.predict_delta_p()
bench = np.random.randn(100, 1)
hold = np.random.randn(1, 100)
eval_result = Evaluation(p_eval, max(n), p, step, threshold, bench,
hold, 100, True, 5000, 5000, 4)
returns = eval_result.periodic_return()[0]
market = eval_result.periodic_return()[1]
temp = Calculate_index(returns, market, 0.05, 0.04, 1, 500, 4)
sharpe = temp.sharpe_ratio()
return sharpe, eval_result.visual_account(
threshold)[0], eval_result.visual_account(threshold)[2]
def run_model(self, n, n_cluster, n_effective, step, threshold,
is_ultimate):
"""
:param n: 一组设定时间序列长度的数组
:param n_cluster: int, 聚类个数
:param n_effective: 有效聚类个数
:param step: 步长
:param threshold: 阈值
:param is_ultimate: 波尔数，是否运行最终结果
:return:夏普指数，账户余额
"""
data = self.input_data
data = data.values.reshape(1, -1)
index = round(len(data[0]) / 3)
p1 = data[0, 0:index]
p2 = data[0, index:2 * index]
p_eval = data[0, 2 * index:]
data_pro = ProcessData(p1, n, n_cluster, n_effective)
effective = data_pro.select_effective_clusters()
test_model = Prediction(effective, p2, n, p_eval)
p = test_model.predict_delta_p()
bench = np.random.randn(100, 1)
hold = np.random.randn(1, 100)
eval_result = Evaluation(p_eval, max(n), p, step, threshold, bench,
hold, 100, True, 5000, 5000, 4)
drawdown = eval_result.calculate_max_drawdown()
#returns = eval_result.periodic_return()[0]
#market = eval_result.periodic_return()[1]
#temp = Calculate_index(returns, market, 0.05, 0.04, 1, 500, 4)
# sharpe = temp.sharpe_ratio()
if is_ultimate:
rate = eval_result.correct_rate()
print("Correct rate:", rate)
eval_result.plot_price_and_profit()
return drawdown, eval_result.visual_account(
threshold)[0], eval_result.visual_account(threshold)[2]
def intensive_training(self):
total_w0 = 0
total_w1 = 0
total_w2 = 0
total_w3 = 0
total_w4 = 0
for i in range(0, 10):
index = round(len(self.prices) / 10)
train_i = self.prices[i * index:(i + 1) * index]
pre = Prediction(self.s, train_i, self.n, self.price_3)
[w0, w1, w2, w3, w4] = pre.find_parameters_w()
total_w0 += w0
total_w1 += w1
total_w2 += w2
total_w3 += w3
total_w4 += w4
best_w0 = total_w0 / 10
best_w1 = total_w1 / 10
best_w2 = total_w2 / 10
best_w3 = total_w3 / 10
best_w4 = total_w4 / 10
return best_w0, best_w1, best_w2, best_w3, best_w4
class Prediction:
def __init__(self, s, prices, n, price_3):
self.prices = prices
self.s = s
self.n = n
self.price_3 = price_3
'''
创建一个函数表示贝叶斯模型的公式
'''
def bayesian_model(self, x, center):
num = 0
den = 0
getcontext().prec = 10
getcontext().Emax = 99999999
getcontext().Emin = -99999999
for i in range(len(center)):
x_i = center[i, :len(x)]
y_i = center[i, len(x)]
expect = np.exp(Decimal(-0.35 * norm(x - x_i)**2))
num += expect * Decimal(y_i)
den += expect
return num / den
'''
建立delta_p和delta_p1,delta_p2,delta_p3之间的关系，并确定自变量X和应变量Y
'''
def variance_determine(self):
X = np.empty((len(self.prices) - max(self.n) - 1, 4))
Y = np.empty((len(self.prices) - max(self.n) - 1, 1))
for i in range(max(self.n), len(self.prices) - 1):
delta_p = Decimal(self.prices[i + 1] - self.prices[i])
delta_p1 = self.bayesian_model(self.prices[i - self.n[0]:i],
self.s[0][0])
delta_p2 = self.bayesian_model(self.prices[i - self.n[1]:i],
self.s[1][0])
delta_p3 = self.bayesian_model(self.prices[i - self.n[2]:i],
self.s[2][0])
delta_p4 = self.bayesian_model(self.prices[i - self.n[3]:i],
self.s[3][0])
X[i - max(self.n), :] = [delta_p1, delta_p2, delta_p3, delta_p4]
Y[i - max(self.n)] = delta_p
return X, Y
'''
对第二个数据集中的X，Y进行线性拟合，确定参数w0, w1, w2, w3, w4
'''
def find_parameters_w(self):
X, Y = self.variance_determine()
clf = linear_model.LinearRegression()
clf.fit(X, Y)
w0 = clf.intercept_
w1 = clf.coef_[0, 0]
w2 = clf.coef_[0, 1]
w3 = clf.coef_[0, 2]
w4 = clf.coef_[0, 3]
return w0, w1, w2, w3, w4
'''
利用拟合出的参数，根据XY的线性关系，求出各时点的价格变化，并储存在矩阵delta_p中
'''
def predict_delta_p(self):
w0, w1, w2, w3, w4 = self.find_parameters_w()
delta_p = []
w0 = float(w0)
for i in range(max(self.n), len(self.price_3) - 1):
delta_p1 = self.bayesian_model(self.price_3[i - self.n[0]:i],
self.s[0][0])
delta_p2 = self.bayesian_model(self.price_3[i - self.n[1]:i],
self.s[1][0])
delta_p3 = self.bayesian_model(self.price_3[i - self.n[2]:i],
self.s[2][0])
delta_p4 = self.bayesian_model(self.price_3[i - self.n[3]:i],
self.s[3][0])
dp = Decimal(w0) + Decimal(w1) * delta_p1 + Decimal(w2) * delta_p2 + Decimal(w3) * delta_p3\
+ Decimal(w4) * delta_p4
delta_p.append(float(dp))
return delta_p
if __name__ == '__main__':
import pandas as pd
from bayesian_model.data_processor import ProcessData
from bayesian_model.bayesian_regression import Prediction
p1 = pd.read_csv('.//p1.csv')
p2 = pd.read_csv('.//p2.csv')
p3 = pd.read_csv('.//p3.csv')
n = [90, 180, 360, 720]
price_reshaped_1 = p1.values.reshape((1, -1))
price_reshaped_2 = p2.values.reshape((1, -1))
price_reshaped_3 = p3.values.reshape((1, -1))
data_pro = ProcessData(price_reshaped_1, n, 100, 20)
effective = data_pro.select_effective_clusters()
test_model = Prediction(effective, price_reshaped_2, n,
price_reshaped_3)
p = test_model.predict_delta_p()
print(p)
def error(self):
pre = Prediction(self.s, self.prices, self.n, self.price_3)
predicted_dp = pre.predict_delta_p()
print(predicted_dp)
"""
import matplotlib.pyplot as plt
p1 = pd.read_csv('.//p1.csv')
p2 = pd.read_csv('.//p2.csv')
p3 = pd.read_csv('.//p3.csv')
# def read_data():
#p1 = pd.read_csv('.//price4.csv')
#p2 = pd.read_csv('.//price5.csv')
#p3 = pd.read_csv('.//price6.csv')
n = [90, 180, 360, 720]
price_reshaped_1 = p1.values.reshape((1, -1))[0, :]
price_reshaped_2 = p2.values.reshape((1, -1))[0, :]
price_reshaped_3 = p3.values.reshape((1, -1))[0, :]
data_pro = ProcessData(price_reshaped_1, n, 100, 20)
effective = data_pro.select_effective_clusters()
test_model = Prediction(effective, price_reshaped_2, n, price_reshaped_3)
p = test_model.predict_delta_p()
print(p)
actual_dp = []
for i in range(len(price_reshaped_3) - 1):
actual_dp.append(price_reshaped_3[i + 1] - price_reshaped_3[i])
# print(actual_dp)
eval = Evaluation(price_reshaped_3, 720, p, 2, 0.07, True, 5000, 5000, 100)
eval.plot_price_and_profit()
eval.correct_rate()
#eval.calculate_max_drawdown()
print(eval.sharpe_ratio())
# delta = eval.visual_account()
returns = eval.periodic_return()[0]
market = eval.periodic_return()[1]