def volval():
lm = pd.read_csv(
"/Users/s1830409/Google ドライブ/outputData/minashi-learn.csv")
lm.set_index('コード', inplace=True)
nk225 = n225.DfNk225()
# 株価水準
val = nk225.data.mean()
# 日次リターン
nk225.data = nk225.data.pct_change()
nk225.data.fillna(0, inplace=True)
# ボラティリティ
vol = nk225.data.std() * np.sqrt(250)
volval = vol * val
# err比率
rat = lm['100000'] / lm['answer']
plt.scatter(rat, volval[1:])
plt.yscale('log')
plt.xscale('log')
plt.show()
def val():
lm = pd.read_csv(
"/Users/s1830409/Google ドライブ/outputData/minashi-learn.csv")
lm.set_index('コード', inplace=True)
nk225 = n225.DfNk225()
# 株価水準
val = nk225.data.mean()
# err比率
rat = lm['100000'] / lm['answer']
plt.scatter(rat, val[1:])
plt.yscale('log')
plt.xscale('log')
plt.show()
def track():
# load learned minashi
filePath = "/Users/s1830409/Google ドライブ/outputData/minashi-learn.csv"
minashiDf = pd.read_csv(filePath)
minashiDf.set_index('コード', inplace=True)
sDate = '2018/10/01'
eDate = '2019/03/15'
# make225sim
nk = n225.DfNk225()
ansData = nk.makeOrigin225(sDate, eDate, minashiDf['answer'].values)
s100Data = nk.makeOrigin225(sDate, eDate, minashiDf['100'].values)
s1000Data = nk.makeOrigin225(sDate, eDate, minashiDf['1000'].values)
s10000Data = nk.makeOrigin225(sDate, eDate, minashiDf['10000'].values)
s100000Data = nk.makeOrigin225(sDate, eDate, minashiDf['100000'].values)
plt.plot(ansData['NK'], label="answer")
plt.plot(s100Data['NK'], label="100")
plt.plot(s1000Data['NK'], label="1000")
plt.plot(s10000Data['NK'], label="10000")
plt.plot(s100000Data['NK'], label="100000")
plt.legend()
#plt.show()
plt.savefig("/Users/s1830409/Google ドライブ/outputData/ori225.png")
oData = pd.DataFrame({
'answer': ansData['NK'],
'100': s100Data['NK'],
'1000': s1000Data['NK'],
'10000': s10000Data['NK'],
'100000': s100000Data['NK'],
})
oData.to_csv("/Users/s1830409/Google ドライブ/outputData/ori225.csv")
import nk225data as n225
import pandas as pd
import tensorflow as tf
if __name__ == '__main__':
data = n225.DfNk225()
# 除数を学習
# 除数
vD = tf.Variable(225, dtype=tf.float64)
# 株価
hPrice = tf.placeholder(tf.float64, [len(data.minashiF)])
# モデル構築
model = hPrice * tf.convert_to_tensor(data.minashiF)
model = tf.reduce_sum(model) / vD
# Minimize
hAnswer = tf.placeholder(tf.float64)
loss = tf.reduce_mean(tf.square(model - hAnswer))
optimizer = tf.train.GradientDescentOptimizer(0.000001)
train = optimizer.minimize(loss)
# Before starting, initialize the variables. We will 'run' this firs
init = tf.global_variables_initializer()
# Launch the graph.
sess = tf.Session()
sess.run(init)
for step in range(100):