Constant

from __future__ import division
import os
import pytz
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

from catalyst.api import order,record, symbols, order_target_percent,schedule_function,date_rules,time_rules
from catalyst.utils.run_algo import run_algorithm
from catalyst.exchange.utils.stats_utils import extract_transactions
def initialize(context):
    # Portfolio assetss list
    context.assets = symbols('btc_usd', 'eth_usd', 'ltc_usd', 'xrp_usd',
                             'xmr_usd')

    context.bought = False
    risk_level = 5
    risk_based_allocation = {0: (0.2,0.2,0.2,0.2,0.2),
                             1: (0.2,0.2,0.2,0.2,0.2),
                             2: (0.2,0.2,0.2,0.2,0.2),
                             3: (0.2,0.2,0.2,0.2,0.2),
                             4: (0.2,0.2,0.2,0.2,0.2),
                             5: (0.198,0.198,0.198,0.198,0.198),
                             6: (0.2,0.2,0.2,0.2,0.2),
                             7: (0.2,0.2,0.2,0.2,0.2),
                             8: (0.2,0.2,0.2,0.2,0.2),
                             9: (0.2,0.2,0.2,0.2,0.2),
                             10: (0.2,0.2,0.2,0.2,0.2)}
    #Saves the weights to easily access during rebalance
    context.target_allocation = dict(zip(context.assets, risk_based_allocation[risk_level]))
    schedule_function(rebalanceInit, date_rules.every_day(),time_rules.every_minute())
    #schedule_function(rebalanceInit, date_rules.week_start(days_offset=3),time_rules.market_open(minutes=480))

    #Same as periodic threshold
    #schedule_function(rebalanceBB, date_rules.week_start(days_offset=3),time_rules.market_open(minutes=480))

    #schedule_function(rebalanceInit, date_rules.every_day(),time_rules.market_price(hours=1))

    context.i=0
    context.base_price = None
def rebalanceInit(context, data):
    #total value of portfolio
    value = context.portfolio.portfolio_value
    for asset in context.assets:
        if (context.target_allocation[asset] == 0):
            continue
        current_holdings = data.current(asset,'price') * context.portfolio.positions[asset].amount
        weight = current_holdings/value
        growth = float(weight) / float(context.target_allocation[asset])
        #if weights of any position exceed threshold, trigger rebalance

        if (growth >= 1.02 or growth <= 0.98):
            rebalance(context, data)
            break
    print("No need to rebalance!")

def rebalance(context, data):

    for asset in context.assets:
        current_weight = (data.current(asset, 'price') * context.portfolio.positions[asset].amount) / context.portfolio.portfolio_value
        target_weight = context.target_allocation[asset]
        distance = current_weight - target_weight
        if (distance > 0):
            amount = -1 * (distance * context.portfolio.portfolio_value) / data.current(asset,'price')
            if (int(amount) == 0):
                continue
            print("Selling " + str(int(amount)) + " shares of " + str(asset))
            order(asset, amount)
    for asset in context.assets:
        current_weight = (data.current(asset, 'price') * context.portfolio.positions[asset].amount) / context.portfolio.portfolio_value
        target_weight = context.target_allocation[asset]
        distance = current_weight - target_weight
        if (distance < 0):
            amount = -1 * (distance * context.portfolio.portfolio_value) / data.current(asset,'price')
            if (int(amount) == 0):
                continue
            print("Buying " + str(int(amount)) + " shares of " + str(asset))
            order(asset, amount)

    for asset in context.assets:
            order_target_percent(asset, context.target_allocation[asset])