def main(symbol):
t1 = datetime.now()
t2 = t1 - timedelta(days=PAST_DAYS)
df = DataReader(symbol, FIN_SERVICE_PROVIDER , t2, t1)
print df.head()
print '...' * 20
print df.tail()
return df
shock = np.zeros(days)
drift = np.zeros(days)
for x in range(1, days):
shock[x] = np.random.normal(loc=mu * dt, scale=sigma * np.sqrt(dt))
drift[x] = mu * dt
price[x] = price[x - 1] + (price[x - 1] * (drift[x] + shock[x]))
return price
# In[18]:
AAPL.head()
# In[19]:
# Running the Monte Carlo simulation a hundred times
start_price = 113.790001
for run in range(100):
plt.plot(stock_monte_carlo(start_price, days, mu, sigma))
plt.xlabel('Days')
plt.ylabel('Price')
plt.title('Monte Carlo Simulation for Apple')
# In[ ]:
shock = np.zeros(days)
drift = np.zeros(days)
for x in range(1,days):
shock[x] = np.random.normal(loc=mu*dt,scale=sigma*np.sqrt(dt))
drift[x] = mu * dt
price[x] = price[x-1] + (price[x-1]* (drift[x] + shock[x]))
return price
# In[18]:
AAPL.head()
# In[19]:
# Running the Monte Carlo simulation a hundred times
start_price = 113.790001
for run in range(100):
plt.plot(stock_monte_carlo(start_price,days,mu,sigma))
plt.xlabel('Days')
plt.ylabel('Price')
plt.title('Monte Carlo Simulation for Apple')
shock = np.zeros(days)
drift = np.zeros(days)
for x in range(1,days):
shock[x] = np.random.normal(loc=mu*dt,scale=sigma*np.sqrt(dt))
drift[x] = mu * dt
price[x] = price[x-1] + (price[x-1]* (drift[x] + shock[x]))
return price
# In[19]:
TSLA.head()
# In[20]:
# Running the Monte Carlo simulation a hundred times
start_price = 226.899994
for run in range(100):
plt.plot(stock_monte_carlo(start_price,days,mu,sigma))
plt.xlabel('Days')
plt.ylabel('Price')
plt.title('Monte Carlo Simulation for Tesla Motors')
import pandas as pd
from datetime import datetime
from datetime import *
from pandas.io.data import DataReader
#TECHM.NS,HCLTECH.NS
#axis = DataReader("AXISBANK.NS","yahoo",datetime(2013,3,1),datetime(2014,3,31))
nifty = DataReader("^NSEI","yahoo",datetime(2013,5,1),datetime(2013,5,25))
bn=DataReader("^NSEBANK","yahoo",datetime(2013,5,1),datetime(2013,5,25))
#print spx.head()
#print ss0.head()
#print axis.tail()
print nifty.tail()['Close'],bn.tail()['Close']
print nifty.head()['Close'],bn.head()['Close']
#axis_daily= axis.tail().diff()['Close']
#nifty_daily=nifty.tail().diff()['Close']
#axis_daily= axis.diff()['Close']
nifty_daily=nifty.diff()['Close']
bn_daily=bn.diff()['Close']
#daily_return = (axis_daily*250 ) - (50*nifty_daily)
daily_return = (bn_daily*25)- (50*nifty_daily)
nifty_daily_return =50*(nifty_daily)
bn_daily_return =25*(bn_daily)
print -nifty_daily_return.sum()+bn_daily_return.sum()
both = pd.DataFrame(data = {'nifty': 50*nifty.diff()['Adj Close'], 'bn': 25*bn.diff()['Adj Close']})
print both.plot()
top10_codes = [
'005930.KS','005380.KS','012330.KS','005490.KS','000660.KS',
'035420.KS','005935.KS','000270.KS','055550.KS','015760.KS' ]
top5_codes = [
'005930.KS','005380.KS','012330.KS','005490.KS','000660.KS' ]
start = datetime(2014, 1, 1)
end = datetime(2014, 12, 31)
df = DataReader(top10_codes, 'yahoo', start=start, end=end)
df = df['Adj Close']
print(df.head())
df = df[top10_codes]
# df = df[top5_codes]
# print(df.head())
code_names = {
'005930.KS':'Samsung Elec', '005380.KS':'Hyundai Motor',
'012330.KS':'Hyundai Mobis', '005490.KS':'POSCO',
'000660.KS':'SK Hynix','035420.KS':'Naver',
'005935.KS':'Samsung Elec(Prep)', '000270.KS':'Kia Motor',
'055550.KS':'Shinhan', '015760.KS':'Korea Elc Pwr' }
df = df.rename(columns=code_names)
# print(df.head())
from pandas.io.data import DataReader
top10_codes = [
'005930.KS', '005380.KS', '012330.KS', '005490.KS', '000660.KS',
'035420.KS', '005935.KS', '000270.KS', '055550.KS', '015760.KS'
]
top5_codes = ['005930.KS', '005380.KS', '012330.KS', '005490.KS', '000660.KS']
start = datetime(2014, 1, 1)
end = datetime(2014, 12, 31)
df = DataReader(top10_codes, 'yahoo', start=start, end=end)
df = df['Adj Close']
print(df.head())
df = df[top10_codes]
# df = df[top5_codes]
# print(df.head())
code_names = {
'005930.KS': 'Samsung Elec',
'005380.KS': 'Hyundai Motor',
'012330.KS': 'Hyundai Mobis',
'005490.KS': 'POSCO',
'000660.KS': 'SK Hynix',
'035420.KS': 'Naver',
'005935.KS': 'Samsung Elec(Prep)',
'000270.KS': 'Kia Motor',
'055550.KS': 'Shinhan',
# logicals
temps_df.Missoula > 82 #
temps_df[temps_df.Missoula > 82] # only subsetted rows
#####
# loading CSVs
#####
df = pd.read_csv("data/test1.csv")
df.date
df = pd.read_csv("data/test1.csv", parse_dates=["date"])
type(df.date[0])
df.index # numeric
# reimport
df = pd.read_csv("data/test1.csv", parse_dates=["date"], index_col="date")
# load data from the web
from pandas.io.data import DataReader
from datetime import date
from dateutil.relativedelta import relativedelta
goog = DataReader("GOOG", "yahoo", date.today() + relativedelta(months=-3))
goog.head()
goog.tail()
goog.plot(y="Adj Close")
# coding: utf-8
# iPython Demo
get_ipython().magic(u'pylab inline')
import pandas as pd
from pandas.io.data import DataReader
import matplotlib.pyplot as plt
df = DataReader("TSLA", "yahoo", " 20141130", "20151201")
df.head()
plt.plot(df.index, df['Close'])
plt.show()
shock = np.zeros(days)
drift = np.zeros(days)
for x in range(1, days):
shock[x] = np.random.normal(loc=mu * dt, scale=sigma * np.sqrt(dt))
drift[x] = mu * dt
price[x] = price[x - 1] + (price[x - 1] * (drift[x] + shock[x]))
return price
# In[19]:
XOM.head()
# In[20]:
# Running the Monte Carlo simulation a hundred times
start_price = 93.330002
for run in range(100):
plt.plot(stock_monte_carlo(start_price, days, mu, sigma))
plt.xlabel('Days')
plt.ylabel('Price')
plt.title('Monte Carlo Simulation for ExxonMobil')
# In[21]:
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 15 10:40:13 2017
@author: Tom
"""
from pandas.io.data import DataReader
Tickers = [
"HK3188"
];
for ticker in Tickers:
try:
data = DataReader(ticker, 'yahoo')
print(data.head())
except Exception as e:
print("{} failed. {}\n".format(ticker, e))
import datetime
import pandas as pd
from pandas.io.data import DataReader
from matplotlib import pyplot as plt
start = datetime.datetime(2014, 1, 1)
end = datetime.datetime(2014, 12, 31)
#삼성전자
df = DataReader("005930.KS", "yahoo", start, end)
df['MA_5'] = pd.stats.moments.rolling_mean(df['Adj Close'], 5)
df['MA_20'] = pd.stats.moments.rolling_mean(df['Adj Close'], 20)
df['diff'] = df['MA_5'] - df['MA_20']
print(df.head(100))
#골든크로스 데드크로스
prev_key = prev_val = 0
for key, val in df['diff'][1:].iteritems():
if val == 0:
continue
if val * prev_val < 0 and val > prev_val:
print '[golden]', key, val
if val * prev_val < 0 and val < prev_val:
print '[dead]', key, val
prev_key, prev_val = key, val
#골든크로 데드크로스 차트 표시
ax = df[['Adj Close', 'MA_5', 'MA_20']].plot(figsize(16, 6))
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 28 17:10:45 2016
@author: gopal_a
"""
from pandas.io.data import DataReader
ticker = "SPY"
source = 'yahoo'
start = '01/01/2012'
end = '03/22/2014'
spy = DataReader(ticker, source, start, end)
print spy.head()
print spy.tail()
spy.to_csv('D:\\SPY.csv')
shock = np.zeros(days)
drift = np.zeros(days)
for x in range(1,days):
shock[x] = np.random.normal(loc=mu*dt,scale=sigma*np.sqrt(dt))
drift[x] = mu * dt
price[x] = price[x-1] + (price[x-1]* (drift[x] + shock[x]))
return price
# In[18]:
MSFT.head()
# In[19]:
# Running the Monte Carlo simulation a hundred times
start_price = 47.700001
for run in range(100):
plt.plot(stock_monte_carlo(start_price,days,mu,sigma))
plt.xlabel('Days')
plt.ylabel('Price')
plt.title('Monte Carlo Simulation for Microsoft')
shock = np.zeros(days)
drift = np.zeros(days)
for x in range(1, days):
shock[x] = np.random.normal(loc=mu * dt, scale=sigma * np.sqrt(dt))
drift[x] = mu * dt
price[x] = price[x - 1] + (price[x - 1] * (drift[x] + shock[x]))
return price
# In[20]:
GOOG.head()
# In[21]:
# Running the Monte Carlo simulation a hundred times
start_price = 532.192446
for run in range(100):
plt.plot(stock_monte_carlo(start_price, days, mu, sigma))
plt.xlabel('Days')
plt.ylabel('Price')
plt.title('Monte Carlo Simulation for Google')
# In[22]:
shock = np.zeros(days)
drift = np.zeros(days)
for x in range(1, days):
shock[x] = np.random.normal(loc=mu * dt, scale=sigma * np.sqrt(dt))
drift[x] = mu * dt
price[x] = price[x - 1] + (price[x - 1] * (drift[x] + shock[x]))
return price
# In[19]:
TSLA.head()
# In[20]:
# Running the Monte Carlo simulation a hundred times
start_price = 226.899994
for run in range(100):
plt.plot(stock_monte_carlo(start_price, days, mu, sigma))
plt.xlabel('Days')
plt.ylabel('Price')
plt.title('Monte Carlo Simulation for Tesla Motors')
# In[21]:
# In[21]:
###AAPL['Daily Return'].hist(bins=100) Removed, overlayed graphs better
# In[22]:
#New dataframe from ayhoo finance with just the adj closing
closing_df = DataReader(tech_list, 'yahoo',start,end)['Adj Close']
# In[23]:
closing_df.head()
# In[24]:
tech_returns = closing_df.pct_change()
# In[29]:
sns.jointplot('GOOG','MSFT', tech_returns,kind='scatter', color='green')
# In[31]:
sns.pairplot(tech_returns.dropna())
import datetime
import pandas as pd
from pandas.io.data import DataReader
from matplotlib import pyplot as plt
start = datetime.datetime(2014, 1, 1)
end = datetime.datetime(2014, 12, 31)
#삼성전자
df = DataReader("005930.KS", "yahoo", start, end)
df['MA_5'] = pd.stats.moments.rolling_mean(df['Adj Close'], 5)
df['MA_20'] = pd.stats.moments.rolling_mean(df['Adj Close'], 20)
df['diff'] = df['MA_5'] - df['MA_20']
print(df.head(100))
#골든크로스 데드크로스
prev_key = prev_val = 0
for key, val in df['diff'][1:].iteritems():
if val == 0:
continue
if val * prev_val < 0 and val > prev_val:
print '[golden]', key, val
if val * prev_val < 0 and val < prev_val:
print '[dead]', key, val
prev_key, prev_val = key, val
#골든크로 데드크로스 차트 표시
ax = df[['Adj Close', 'MA_5', 'MA_20']].plot(figsize(16,6))
def get_data():
"""symbols are specified on command line
mitre, folio, smm are allowed abbreviations
period defaults to 400 calendar days
"""
start = get_starting_date()
print "\ttoday is %s" % datetime.datetime.today().date()
print "\tstart date is %s\n" % start
series = {}
not_found = []
for index, symbol in enumerate(symbols, 1):
print "\t%6d. fetching %s" % (index, symbol.upper()),
try:
data = DataReader(symbol.upper(),"yahoo", start)
print "\t", type(data), data.shape
print data.head()
print "first five adj close values:", data["Adj Close"][:5]
x = data["Adj Close"]
print "\n\t x starts with ", x[:5]
z = pd.DataFrame(index = data.index, \
data = x.values, columns = [symbol])
print "\n\tz\n", z.head()
series[symbol] = z
print series[symbol]
Symbol(symbol)
Symbol.dict[symbol].adjclose = series[symbol]
print
except urllib2.HTTPError:
print "\tnot found"
not_found.append(symbol)
for symbol in not_found:
symbols.remove(symbol)
print "\tnot found:", symbol
df = series[symbols[0]]
print "head and tail for", symbols[0]
print df.head()
print df.tail()
print df.shape
print "\n", "_" * 40, "\n"
#-------------------------
for symbol in symbols[1:]:
new = series[symbol]
df = pd.merge(left = df,
right = new,
left_index = True,
right_index = True,
how = "outer")
print "\njoined df"
print df.head()
print df.tail()
print df.shape
print "_" * 30
print "\ntotal returns"
for symbol_name in Symbol.dict:
symbol = Symbol.dict[symbol_name]
series = df[symbol_name].dropna()
total_return = series[-1] - series[0]
total_return_pct = 100.0 * total_return / series[0]
print "%10s" % symbol.symbol,
print "total return = %10.2f " % total_return,
print "total return = %10.2f" % total_return_pct, "%"
symbol.total_return = total_return
symbol.total_return_pct = total_return_pct
symbol.trading_days = series.shape[0]
print "%10s has %6d trading days" % \
(symbol_name, series.shape[0])
calendar_days = (
series.index[-1] - series.index[0]).days + 1
print "%10s has %6d calendar days" % (symbol_name, calendar_days)
symbol.calendar_days = calendar_days
years = calendar_days / 365.25
arr = (1.0 + total_return_pct / 100) ** ( 1.0 / years) - 1.0
symbol.first_day = series.index[0]
symbol.last_day = series.index[-1]
symbol.calendar_days = (symbol.last_day - symbol.first_day).days + 1
symbol.arr = arr
symbol.years = years
symbol.max_draw_down = max_draw_down_f(symbol, series)
symbol.series = series
print
return df
shock = np.zeros(days)
drift = np.zeros(days)
for x in range(1,days):
shock[x] = np.random.normal(loc=mu*dt,scale=sigma*np.sqrt(dt))
drift[x] = mu * dt
price[x] = price[x-1] + (price[x-1]* (drift[x] + shock[x]))
return price
# In[20]:
GOOG.head()
# In[21]:
# Running the Monte Carlo simulation a hundred times
start_price = 532.192446
for run in range(100):
plt.plot(stock_monte_carlo(start_price,days,mu,sigma))
plt.xlabel('Days')
plt.ylabel('Price')
plt.title('Monte Carlo Simulation for Google')
data = psql.read_frame(sqltxt, con)
sqlDropTxt = 'DROP TABLE "main"."{}"'.format(tb)
#print sqlDropTxt
psql.execute(sqlDropTxt, con)
con.commit()
psql.write_frame( data, tb, con)
con.commit()
con.close()
start = datetime(2007,1,1)
end = datetime.date(datetime.now())
data = DataReader("^GSPC", "yahoo", start, end)
data = data.reset_index()
data.Date = [unicode(d) for d in data.Date]
data.Date[0]
data.head()
con = sqlite3.connect("/home/phcostello/Documents/Data/FinanceData.sqlite")
try:
psql.write_frame( data, "SP500", con)
con.commit()
except Exception as e:
print "Problems with {}".format(series[0]), e
con.close()
currencyfig2.savefig('currencyfig2.png')
currency.loc[:, ('Daily Return')] = currency[[0]].pct_change()
currencyfig3 = currency[[4]].plot(figsize=(10,4), legend=True, linestyle='--', marker='o')
currencyfig3 = currencyfig3.get_figure()
currencyfig3.savefig('currencyfig3.png')
currencyfig4 = sns.distplot(currency[['Daily Return']].dropna(), color='purple')
currencyfig4 = currencyfig4.get_figure()
currencyfig4.savefig('currencyfig4.png')
closing_df = DataReader(fred_currencies, 'fred', start, end)
print '\nDaily Close Snapshot'
print '\n', closing_df.head()
fred_ret = closing_df.pct_change()
print '\nDaily Return Snapshot\n'
print '\n', fred_ret.head()
print "\nSee how currency pairs correlate...\n"
currency1 = raw_input("Enter Currency 1: ")
if currency1 == 'DEXUSEU':
currency1 = fred_ret[['DEXUSEU']].dropna()
currency1.rename(columns = {'DEXUSEU':'Return'}, inplace = True)
elif currency1 == 'DEXUSAL':
currency1 = fred_ret[['DEXUSAL']].dropna()
currency1.rename(columns = {'DEXUSAL':'Return'}, inplace = True)
elif currency1 == 'DEXUSUK':
shock = np.zeros(days)
drift = np.zeros(days)
for x in range(1,days):
shock[x] = np.random.normal(loc=mu*dt,scale=sigma*np.sqrt(dt))
drift[x] = mu * dt
price[x] = price[x-1] + (price[x-1]* (drift[x] + shock[x]))
return price
# In[18]:
AMZN.head()
# In[19]:
# Running the Monte Carlo simulation a hundred times
start_price = 307.850006
for run in range(100):
plt.plot(stock_monte_carlo(start_price,days,mu,sigma))
plt.xlabel('Days')
plt.ylabel('Price')
plt.title('Monte Carlo Simulation for Amazon')
legend=True,
linestyle='--',
marker='o')
# In[3]:
sns.distplot(MSFT['Daily Return'].dropna(), bins=100, color='purple')
# In[55]:
# Closing prices
closing_df = DataReader(['AAPL', 'GOOG', 'MSFT', 'AMZN'], 'yahoo', start,
end)['Adj Close']
# In[57]:
closing_df.head()
# In[8]:
# Make a new tech returns DataFrame
tech_rets = closing_df.pct_change()
# Using joinplot to compare the daily returns of Google and Microsoft
sns.jointplot('GOOG', 'MSFT', tech_rets, kind='scatter')
# In[7]:
sns.pairplot(tech_rets.dropna())
# In[ ]:
plt.show()
AAPL['Daily Return'] = AAPL['Adj Close'].pct_change()
AAPL['Daily Return'].plot(figsize=(10, 4), legend=True, linestyle='--', marker='o')
plt.show()
sns.distplot(AAPL['Daily Return'].dropna(), bins=100, color='purple')
plt.show()
AAPL['Daily Return'].hist(bins=100)
plt.show()
closing_df = DataReader(tech_list, 'yahoo', start, end)['Adj Close']
print(closing_df.head())
tech_rets = closing_df.pct_change()
print(tech_rets.head())
sns.jointplot('GOOG', 'GOOG', tech_rets, kind = 'scatter', color='seagreen')
plt.show()
sns.jointplot('GOOG', 'MSFT', tech_rets, kind='scatter')
plt.show()
sns.pairplot(tech_rets.dropna())
plt.show()
returns_fig = sns.PairGrid(tech_rets.dropna())
returns_fig.map_upper(plt.scatter, color='purple')
currencyfig3 = currency[[4]].plot(figsize=(10, 4),
legend=True,
linestyle='--',
marker='o')
currencyfig3 = currencyfig3.get_figure()
currencyfig3.savefig('currencyfig3.png')
currencyfig4 = sns.distplot(currency[['Daily Return']].dropna(),
color='purple')
currencyfig4 = currencyfig4.get_figure()
currencyfig4.savefig('currencyfig4.png')
closing_df = DataReader(fred_currencies, 'fred', start, end)
print '\nDaily Close Snapshot'
print '\n', closing_df.head()
fred_ret = closing_df.pct_change()
print '\nDaily Return Snapshot\n'
print '\n', fred_ret.head()
print "\nSee how currency pairs correlate...\n"
currency1 = raw_input("Enter Currency 1: ")
if currency1 == 'DEXUSEU':
currency1 = fred_ret[['DEXUSEU']].dropna()
currency1.rename(columns={'DEXUSEU': 'Return'}, inplace=True)
elif currency1 == 'DEXUSAL':
currency1 = fred_ret[['DEXUSAL']].dropna()
currency1.rename(columns={'DEXUSAL': 'Return'}, inplace=True)
elif currency1 == 'DEXUSUK':
