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open = pd.Series(df['open'])
high = pd.Series(df['high'])
low = pd.Series(df['low'])
close = pd.Series(df['close'])
volume = pd.Series(df['volume'])

# pct_change for new column
X['diff'] = y

# Exponential Moving Average

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_ipyw_jlab_nb_ext_conf    0.1.0                    py37_0
_libgcc_mutex             0.1                        main
alabaster                 0.7.12                   py37_0
anaconda                  2019.07                  py37_0
anaconda-client           1.7.2                    py37_0
anaconda-navigator        1.9.7                    py37_0
anaconda-project          0.8.3                      py_0
asn1crypto                0.24.0                   py37_0
astroid                   2.2.5                    py37_0
astropy                   3.2.1            py37h7b6447c_0

yuyasugano

_ipyw_jlab_nb_ext_conf    0.1.0                    py37_0
_libgcc_mutex             0.1                        main
alabaster                 0.7.12                   py37_0
anaconda                  2019.07                  py37_0
anaconda-client           1.7.2                    py37_0
anaconda-navigator        1.9.7                    py37_0
anaconda-project          0.8.3                      py_0
asn1crypto                0.24.0                   py37_0
astroid                   2.2.5                    py37_0
astropy                   3.2.1            py37h7b6447c_0

yuyasugano

# Univariate Statistics
from sklearn.feature_selection import SelectPercentile
select = SelectPercentile(percentile=25)
select.fit(X_train_full, y_train_full.values.ravel())
X_train_selected = select.transform(X_train_full)
X_test_selected = select.transform(X_test_full)
mask = select.get_support()
print(mask)
plt.matshow(mask.reshape(1, -1), cmap='gray_r')
plt.xlabel("Technical Indexes")

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# Model-based Selection
from sklearn.feature_selection import SelectFromModel
select = SelectFromModel(RandomForestClassifier(n_estimators=100, random_state=42),
                        threshold="1.25*mean")
select.fit(X_train_full, y_train_full.values.ravel())
X_train_model = select.transform(X_train_full)
print(X_train_model.shape)
X_test_model = select.transform(X_test_full)
mask = select.get_support()
print(mask)

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# Recursive Feature Elimination
from sklearn.feature_selection import RFE
select = RFE(RandomForestClassifier(n_estimators=100, random_state=42),
             n_features_to_select=15)
select.fit(X_train_full, y_train_full.values.ravel())
X_train_rfe = select.transform(X_train_full)
X_test_rfe = select.transform(X_test_full)
mask = select.get_support()
print(mask)
plt.matshow(mask.reshape(1, -1), cmap='gray_r')

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from sklearn.datasets import load_iris
from sklearn.model_selection import ParameterGrid, StratifiedKFold

iris = load_iris()
param_grid = [{'kernel': ['rbf'],
               'C': [0.01, 1],
               'gamma': [0.1, 1]},
              {'kernel': ['linear'],
               'C': [0.01, 1]}]
print("List of parameter grids:\n{}".format(param_grid))

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version: '3'
services:
  elasticsearch:
    build: elasticsearch
    container_name: elasticsearch
    environment:
      - discovery.type=single-node
      - bootstrap.memory_lock=true
      - "ES_JAVA_OPTS=-Xms256m -Xmx256m"
    ulimits:

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{
  "btcjpy" : {
    "aliases" : { },
    "mappings" : {
      "properties" : {
        "close" : {
          "type" : "long"
        },
        "timestamp" : {
          "type" : "date",

yuyasugano

#!/usr/bin/python
import json
import time
import python_bitbankcc
from datetime import datetime
from elasticsearch import Elasticsearch

def get_ticker(pub):
    ret = pub.get_ticker('btc_jpy')
    return ret