# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>

# <codecell>

# Concerned third party
import pandas as pd
from pandas import DataFrame
from pandas import Series
import numpy as np
import math
import copy
import QSTK.qstkutil.qsdateutil as du
import datetime as dt
import QSTK.qstkutil.DataAccess as da
import QSTK.qstkutil.tsutil as tsu
import matplotlib.pyplot as plt
import os
# Preliminary preparation
dt_start = dt.datetime(2007, 1, 1)
dt_end = dt.datetime(2010, 12, 31) 
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt.timedelta(hours = 16))
#ls_symbols = ['A', 'AA', 'AAPL', 'ABC', 'ABT', 'ACE', 'ACN', 'ADBE', 'ADI', 'ADM', 'ADP', 'ADSK', 'AEE', 'AEP', 'AES', 'AET', 'AFL', 'AGN', 'AIG', 'AIV', 'AIZ', 'AKAM', 'ALL', 'ALTR', 'ALXN', 'AMAT', 'AMD', 'AMGN', 'AMP', 'AMT', 'AMZN', 'AN', 'ANF', 'ANR', 'AON', 'APA', 'APC', 'APD', 'APH', 'APOL', 'ARG', 'ATI', 'AVB', 'AVP', 'AVY', 'AXP', 'AZO', 'BA', 'BAC', 'BAX', 'BBBY', 'BBT', 'BBY', 'BCR', 'BDX', 'BEAM', 'BEN', 'BF.B', 'BHI', 'BIG', 'BIIB', 'BK', 'BLK', 'BLL', 'BMC', 'BMS', 'BMY', 'BRCM', 'BRK.B', 'BSX', 'BTU', 'BWA', 'BXP', 'C', 'CA', 'CAG', 'CAH', 'CAM', 'CAT', 'CB', 'CBE', 'CBG', 'CBS', 'CCE', 'CCI', 'CCL', 'CELG', 'CERN', 'CF', 'CFN', 'CHK', 'CHRW', 'CI', 'CINF', 'CL', 'CLF', 'CLX', 'CMA', 'CMCSA', 'CME', 'CMG', 'CMI', 'CMS', 'CNP', 'CNX', 'COF', 'COG', 'COH', 'COL', 'COP', 'COST', 'COV', 'CPB', 'CRM', 'CSC', 'CSCO', 'CSX', 'CTAS', 'CTL', 'CTSH', 'CTXS', 'CVC', 'CVH', 'CVS', 'CVX', 'D', 'DD', 'DE', 'DELL', 'DF', 'DFS', 'DGX', 'DHI', 'DHR', 'DIS', 'DISCA', 'DLTR', 'DNB', 'DNR', 'DO', 'DOV', 'DOW', 'DPS', 'DRI', 'DTE', 'DTV', 'DUK', 'DV', 'DVA', 'DVN', 'EA', 'EBAY', 'ECL', 'ED', 'EFX', 'EIX', 'EL', 'EMC', 'EMN', 'EMR', 'EOG', 'EQR', 'EQT', 'ESRX', 'ESV', 'ETFC', 'ETN', 'ETR', 'EW', 'EXC', 'EXPD', 'EXPE', 'F', 'FAST', 'FCX', 'FDO', 'FDX', 'FE', 'FFIV', 'FHN', 'FII', 'FIS', 'FISV', 'FITB', 'FLIR', 'FLR', 'FLS', 'FMC', 'FOSL', 'FRX', 'FSLR', 'FTI', 'FTR', 'GAS', 'GCI', 'GD', 'GE', 'GILD', 'GIS', 'GLW', 'GME', 'GNW', 'GOOG', 'GPC', 'GPS', 'GS', 'GT', 'GWW', 'HAL', 'HAR', 'HAS', 'HBAN', 'HCBK', 'HCN', 'HCP', 'HD', 'HES', 'HIG', 'HNZ', 'HOG', 'HON', 'HOT', 'HP', 'HPQ', 'HRB', 'HRL', 'HRS', 'HSP', 'HST', 'HSY', 'HUM', 'IBM', 'ICE', 'IFF', 'IGT', 'INTC', 'INTU', 'IP', 'IPG', 'IR', 'IRM', 'ISRG', 'ITW', 'IVZ', 'JBL', 'JCI', 'JCP', 'JDSU', 'JEC', 'JNJ', 'JNPR', 'JOY', 'JPM', 'JWN', 'K', 'KEY', 'KFT', 'KIM', 'KLAC', 'KMB', 'KMI', 'KMX', 'KO', 'KR', 'KSS', 'L', 'LEG', 'LEN', 'LH', 'LIFE', 'LLL', 'LLTC', 'LLY', 'LM', 'LMT', 'LNC', 'LO', 'LOW', 'LRCX', 'LSI', 'LTD', 'LUK', 'LUV', 'LXK', 'LYB', 'M', 'MA', 'MAR', 'MAS', 'MAT', 'MCD', 'MCHP', 'MCK', 'MCO', 'MDT', 'MET', 'MHP', 'MJN', 'MKC', 'MMC', 'MMM', 'MNST', 'MO', 'MOLX', 'MON', 'MOS', 'MPC', 'MRK', 'MRO', 'MS', 'MSFT', 'MSI', 'MTB', 'MU', 'MUR', 'MWV', 'MYL', 'NBL', 'NBR', 'NDAQ', 'NE', 'NEE', 'NEM', 'NFLX', 'NFX', 'NI', 'NKE', 'NOC', 'NOV', 'NRG', 'NSC', 'NTAP', 'NTRS', 'NU', 'NUE', 'NVDA', 'NWL', 'NWSA', 'NYX', 'OI', 'OKE', 'OMC', 'ORCL', 'ORLY', 'OXY', 'PAYX', 'PBCT', 'PBI', 'PCAR', 'PCG', 'PCL', 'PCLN', 'PCP', 'PCS', 'PDCO', 'PEG', 'PEP', 'PFE', 'PFG', 'PG', 'PGR', 'PH', 'PHM', 'PKI', 'PLD', 'PLL', 'PM', 'PNC', 'PNW', 'POM', 'PPG', 'PPL', 'PRGO', 'PRU', 'PSA', 'PSX', 'PWR', 'PX', 'PXD', 'QCOM', 'QEP', 'R', 'RAI', 'RDC', 'RF', 'RHI', 'RHT', 'RL', 'ROK', 'ROP', 'ROST', 'RRC', 'RRD', 'RSG', 'RTN', 'S', 'SAI', 'SBUX', 'SCG', 'SCHW', 'SE', 'SEE', 'SHLD', 'SHW', 'SIAL', 'SJM', 'SLB', 'SLM', 'SNA', 'SNDK', 'SNI', 'SO', 'SPG', 'SPLS', 'SRCL', 'SRE', 'STI', 'STJ', 'STT', 'STX', 'STZ', 'SUN', 'SWK', 'SWN', 'SWY', 'SYK', 'SYMC', 'SYY', 'T', 'TAP', 'TDC', 'TE', 'TEG', 'TEL', 'TER', 'TGT', 'THC', 'TIE', 'TIF', 'TJX', 'TMK', 'TMO', 'TRIP', 'TROW', 'TRV', 'TSN', 'TSO', 'TSS', 'TWC', 'TWX', 'TXN', 'TXT', 'TYC', 'UNH', 'UNM', 'UNP', 'UPS', 'URBN', 'USB', 'UTX', 'V', 'VAR', 'VFC', 'VIAB', 'VLO', 'VMC', 'VNO', 'VRSN', 'VTR', 'VZ', 'WAG', 'WAT', 'WDC', 'WEC', 'WFC', 'WFM', 'WHR', 'WIN', 'WLP', 'WM', 'WMB', 'WMT', 'WPI', 'WPO', 'WPX', 'WU', 'WY', 'WYN', 'WYNN', 'X', 'XEL', 'XL', 'XLNX', 'XOM', 'XRAY', 'XRX', 'XYL', 'YHOO', 'YUM', 'ZION', 'ZMH']
ls_symbols = ['R']
sym = ls_symbols[0]
check = {}
check[sym]=[ ]
ls_keys = ['close', 'actual_close']
dataobj = da.DataAccess('Yahoo')
ldf_data = dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
for s_key in ls_keys:
        d_data[s_key] = d_data[s_key].fillna(method='ffill')
        d_data[s_key] = d_data[s_key].fillna(method='bfill')
        d_data[s_key] = d_data[s_key].fillna(1.0)
df_close = d_data['close']
ldt_timestamps = df_close.index
ma5 = pd.rolling_mean(df_close, 5)
ma20 = pd.rolling_mean(df_close, 20)
for s_sym in ls_symbols:
    for i in range(1, len(ldt_timestamps)):                        
        if (ma5[s_sym].ix[ldt_timestamps[i]] >= ma20[s_sym].ix[ldt_timestamps[i]]) & (ma5[s_sym].ix[ldt_timestamps[i-1]] <= ma20[s_sym].ix[ldt_timestamps[i-1]]):
            check[s_sym].append((i, -1))
        if (ma5[s_sym].ix[ldt_timestamps[i]] <= ma20[s_sym].ix[ldt_timestamps[i]]) & (ma5[s_sym].ix[ldt_timestamps[i-1]] >= ma20[s_sym].ix[ldt_timestamps[i-1]]):
            check[s_sym].append((i, 1))
    if check[s_sym][0][1] == 1:
        del check[s_sym][0]
    if len(check[s_sym]) % 2 == 1:
        del check[s_sym][-1]
ldt_timestamps = df_close.index
# Start wih $100000
for sym in ls_symbols:
    sta = []
    for i in xrange((len(check[sym]))/2):
        b = df_close[sym][check[sym][2*i+1][0]]
        a = df_close[sym][check[sym][2*i][0]]
        sta.append(b/a)
        win = []
        loose = []
        for i in range(len(sta)):
            if (sta[i] - 1) >=0:
                win.append(sta[i] - 1)
            else:
                loose.append(1 - sta[i])
        p = len(win) / float(len(win) + len(loose))
        b = np.mean(win)
        d = np.mean(loose)
        kelly = (( b * p - d * ( 1 - p ) )/( b * d ))
fraction = [kelly, 0.2, 1]
data = DataFrame(0, columns = fraction, index = ldt_timestamps)
for j in fraction:
    wealth = Series(0, index = ldt_timestamps)
    portfolio = Series(0, index = ldt_timestamps)
    holding = Series(0, index = ldt_timestamps)    
    ts = Series(0, index = ldt_timestamps)
    order = check[sym]
    ts[0: order[0][0]+1] = 100000
    for i in xrange(len(order)/2):
        position = ts[order[2*i][0]] * j / df_close[sym][order[2*i][0]]
        position = round(position)
        holding[order[2*i][0]:order[2*i+1][0]] = position
        ts[order[2*i][0]] -= position * df_close[sym][order[2*i][0]]
        ts[order[2*i][0]+1:order[2*i+1][0]+1] += ts[order[2 * i][0]]
        ts[order[2*i+1][0]] += position * df_close[sym][order[2*i+1][0]]
        try:
            ts[order[2*i+1][0]+1:order[2*i+2][0]+1] += ts[order[2*i+1][0]]
        except IndexError:
            ts[order[2*i+1][0]+1:] = ts[order[2*i+1][0]]   
    for i in xrange(len(ldt_timestamps)):
        portfolio[i] = holding[i] * df_close[sym].ix[ldt_timestamps[i]]        
        wealth[i] = portfolio[i] + ts[i]   
    data[j] = wealth   
(a, b, c) = list(data) 
#plt.plot(data[a])
#plt.plot(data[b])
#plt.plot(data[c])
#plt.show()
cut = []
for i in xrange(len(data) - 1 ):
    if data[a][len(data) -1 - i] == data[a][len(data)-2-i]:
        ind = data.index[len(data)-1-i]
        cut.append(ind)
for i in cut:
    data = data.drop(i)    
plt.plot([100000 for i in xrange(len(data[a]))], 'k--' , alpha = 0.5)
plt.plot(data[a], label = a)
plt.plot(data[b], label = b)
plt.plot(data[c], label = c)
#plt.plot(data[d], label = d)
#plt.plot(data[e], label = e)
plt.title("Trading '%s' during 2007/1/1 to 2010/12/31" %sym)
plt.xlabel('Trading days')
plt.ylabel('Money')
plt.legend(loc = 'best')
plt.show()


# <codecell>

import random
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from pandas import*
def kelly(win_pro, loose_pro, win_bet, loose_bet):
    return (win_pro*win_bet - loose_pro*loose_bet)/(win_bet*loose_bet)
N = 500
kelly = kelly(0.45,0.55,0.54,0.34)
fraction = [kelly, 0.1, 0.5, 0.8]
coin = []
for i in xrange(N):
    c = random.random()
    if c <= 0.45:
        coin.append(1)
    else:
        coin.append(-1)
data = DataFrame(0, columns = fraction, index = [i for i in xrange(N+1)])
for j in fraction:
    cash = [10000]
    for i in xrange(N):
        if coin[i] == 1:
            cash.append(cash[i]*(1 + j * 0.54))
        else:
            cash.append(cash[i]*(1 - j * 0.34))
    data[j] = cash
(a,b,c,d) = list(data)
plt.plot([10000 for i in xrange(len(data[a]))], 'k--' , alpha = 0.5)
plt.plot(data[a], label = a)
plt.plot(data[b], label = b)
plt.plot(data[c], label = c)
plt.plot(data[d], label = d)
#plt.plot(data[e], label = e)
plt.title("Simulation by tossing baised coins")
plt.xlabel('times')
plt.ylabel('Money')
plt.legend(loc = 'best')
plt.show()      
        

# <codecell>



# <codecell>

coin[:20]

# <codecell>