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#!/usr/bin/python
import json
import pandas as pd
import seaborn as sns
import python_bitbankcc

# Ticker information
def get_btc(obj):
    ret = obj.get_ticker('btc_jpy')
    return ret

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# df copy
df_ = df.copy()
df_["ma25"] = df_.close.rolling(window=25).mean()
df_["ma75"] = df_.close.rolling(window=75).mean()
df_["diff"] = df_.ma25-df_.ma75
df_["unixtime"] = [datetime.timestamp(t) for t in df.index]

# line and Moving Average
xdate = [x.date() for x in df_.index]
plt.figure(figsize=(15,5))

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def Bollinger(df, window=25):
    df1 = df.copy()
    df1["ma"] = df1.close.rolling(window=window).mean()
    df1["sigma"] =  df1.close.rolling(window=window).std()
    df1["ma+2sigma"] = df1.ma + 2*df1.sigma
    df1["ma-2sigma"] = df1.ma - 2*df1.sigma
    df1["diffplus"] = df1.close - df1["ma+2sigma"] 
    df1["diffminus"] = df1["ma-2sigma"] - df1.close
    s_up = df1[df1["diffplus"] > 0]["close"]
    s_down = df1[df1["diffminus"] > 0]["close"]

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def MACD(df):
    df1 = df.copy()
    df1["MACD"] = df1.close.ewm(span=12, min_periods=1).mean() - df1.close.ewm(span=26, min_periods=1).mean()
    df1["signal"] = df1.MACD.ewm(span=9, min_periods=1).mean()
    df1["macd_diff"] = df1["MACD"] - df1["signal"]

    xdate = [x.date() for x in df1.index]
    plt.figure(figsize=(15,10))
    # plot the original
    plt.subplot(211)

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def plot_RSI(df, window):
    df1 = df.copy()
    diff = df1.close.diff(periods=1).values
    xdate = [x.date() for x in df1.index]
    RSI = []
    for i in range(window+1, len(xdate)):
        neg = 0
        pos = 0
        for value in diff[i-window:i+1]:
            if value > 0:

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from pubnub.pubnub import PubNub
from pubnub.enums import PNStatusCategory
from pubnub.callbacks import SubscribeCallback
from pubnub.pnconfiguration import PNConfiguration

SUBSCRIBE_KEY = 'sub-c-e12e9174-dd60-11e6-806b-02ee2ddab7fe'
TICKER_CHANNEL = 'ticker_btc_jpy'

class BitbankSubscriberCallback(SubscribeCallback):
    def presence(self, pubnub, presence):

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def SMA_TaLib(df):
    df1 = df.copy()
    df1["ma5"] = talib.SMA(df1['close'], timeperiod=5)
    df1["ma15"] = talib.SMA(df1['close'], timeperiod=15)
    df1["diff"] = df1.ma5 - df1.ma15
    df1["unixtime"] = [datetime.timestamp(t) for t in df1.index]

    # line and Moving Average
    fig = plt.figure(figsize=(15,5))
    ax = fig.add_subplot(1, 1, 1)

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# pct_change
f = lambda x: 1 if x>0.0001 else -1 if x<-0.0001 else 0 if -0.0001<=x<=0.0001 else np.nan
y = df.rename(columns={'close': 'y'}).loc[:, 'y'].pct_change(1).shift(-1).fillna(0)
X = df.copy()
y_ = pd.DataFrame(y.map(f), columns=['y'])
df_ = pd.concat([df, y_], axis=1)

# check the shape
print('----------------------------------------------------------------------------------------')
print('X shape: (%i,%i)' % X.shape)

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from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score

X_train, X_test, y_train, y_test = train_test_split(X_, y_, test_size=0.33, random_state=42)
print('X_train shape: {}'.format(X_train.shape))

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pragma solidity ^0.4.23;

import "./CowBreeding.sol";
import "openzeppelin-solidity/contracts/token/ERC721/ERC721.sol";

/**
 * @title ERC721 compatible Cow Standard Basic Implementation
 * @dev Implements Cow transfer with inherited OpenZeppelin ERC721
 */
contract CowOwnership is CowBreeding, ERC721 {