import datetime as dt
import numpy as np
import pandas as pd
from dyplot.dygraphs import Dygraphs
if __name__ == '__main__':
foo = np.random.rand(100)
boo = pd.Series(foo, index=range(0,100))
dg = Dygraphs(boo.index, "date")
dg.plot(series="Random", mseries=boo)
dg.set_options(title="Tutorial 0")
div = dg.savefig(csv_file="tutorial0.csv", div_id="demodiv", js_vid="jid", html_file="tutorial0.html")
print(div)
import datetime as dt
from finpy.financial.equity import get_tickdata
import finpy.utils.fpdateutil as du
from finpy.financial.portfolio import Portfolio
from dyplot.dygraphs import Dygraphs
if __name__ == '__main__':
dt_timeofday = dt.timedelta(hours=16)
dt_start = dt.datetime(2010, 1, 1)
dt_end = dt.datetime(2010, 12, 31)
ls_symbols = ['AAPL', '$RUA']
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_data = get_tickdata(ls_symbols=ls_symbols,
ldt_timestamps=ldt_timestamps)
all_stocks = Portfolio(all_data, 0, ldt_timestamps, [])
dg = Dygraphs(ldt_timestamps, "date")
dg.plot(series="AAPL", mseries=all_data["AAPL"]['close'], axis='y2')
dg.plot(series="Russel 3000", mseries=all_data["$RUA"]['close'])
max_ratio = max(
all_stocks.normalized("AAPL").max(),
all_stocks.normalized("$RUA").max())
min_ratio = min(
all_stocks.normalized("AAPL").min(),
all_stocks.normalized("$RUA").min())
max_ratio *= 1.05
min_ratio *= 0.95
dg.set_axis_options(axis='y', valueRange=[all_data["$RUA"]['close'][0]*min_ratio, \
all_data["$RUA"]['close'][0]*max_ratio])
dg.set_axis_options(axis='y2', valueRange=[all_data["AAPL"]['close'][0]*min_ratio, \
all_data["AAPL"]['close'][0]*max_ratio])
dg.annotate('AAPL', '2010-06-21', "B", "Buy on 2010-06-21")
dg.annotate('AAPL', '2010-08-13', "S", "Sell on 2010-08-13")
import os import glob import pandas as pd import numpy as np import dyplot as dp import pylab as P import matplotlib.pyplot as plt import pandas as pd from dyplot.dygraphs import Dygraphs a = pd.Series([1,2,3,4,5,6,7,9,10]) b = pd.Series([1,3,5,9,2,8,5,5,15]) lc= pd.Series([1,3,4,5,6,7,9,3,2]) c = pd.Series([2,4,5,7,8,8,9,4,3]) hc= pd.Series([3,5,7,7,9,11,9,5,8]) dg = Dygraphs(a.index, "index") dg.plot(series="a", mseries=a) dg.plot(series="b", mseries=b) dg.plot(series="c", mseries=c,lseries=lc, hseries=hc) dg.set_options(title="Test") dg.set_axis_options(axis="x",axisLabelColor="red") div = dg.savefig(csv_file=os.getcwd() + "/plot_dynamic/tutorial1.csv", html_file=os.getcwd() + "/plot_dynamic/tutorial1.html")
import os
import glob
import pandas as pd
import numpy as np
import dyplot as dp
import pylab as P
import matplotlib.pyplot as plt
import pandas as pd
from dyplot.dygraphs import Dygraphs
a = pd.Series([1, 2, 3, 4, 5, 6, 7, 9, 10])
b = pd.Series([1, 3, 5, 9, 2, 8, 5, 5, 15])
lc = pd.Series([1, 3, 4, 5, 6, 7, 9, 3, 2])
c = pd.Series([2, 4, 5, 7, 8, 8, 9, 4, 3])
hc = pd.Series([3, 5, 7, 7, 9, 11, 9, 5, 8])
dg = Dygraphs(a.index, "index")
dg.plot(series="a", mseries=a)
dg.plot(series="b", mseries=b)
dg.plot(series="c", mseries=c, lseries=lc, hseries=hc)
dg.set_options(title="Test")
dg.set_axis_options(axis="x", axisLabelColor="red")
div = dg.savefig(csv_file=os.getcwd() + "/plot_dynamic/tutorial1.csv",
html_file=os.getcwd() + "/plot_dynamic/tutorial1.html")
import datetime as dt
from finpy.financial.equity import get_tickdata
import finpy.utils.fpdateutil as du
from finpy.financial.portfolio import Portfolio
from dyplot.dygraphs import Dygraphs
if __name__ == '__main__':
dt_timeofday = dt.timedelta(hours=16)
dt_start = dt.datetime(2010, 1, 1)
dt_end = dt.datetime(2010, 12, 31)
ls_symbols = ['AAPL','$RUA']
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_data = get_tickdata(ls_symbols=ls_symbols, ldt_timestamps=ldt_timestamps)
all_stocks = Portfolio(all_data, 0, ldt_timestamps, [])
dg = Dygraphs(ldt_timestamps, "date")
dg.plot(series="AAPL", mseries=all_data["AAPL"]['close'], axis='y2')
dg.plot(series="Russel 3000", mseries=all_data["$RUA"]['close'])
max_ratio = max(all_stocks.normalized("AAPL").max(), all_stocks.normalized("$RUA").max())
min_ratio = min(all_stocks.normalized("AAPL").min(), all_stocks.normalized("$RUA").min())
max_ratio *= 1.05
min_ratio *= 0.95
dg.set_axis_options(axis='y', valueRange=[all_data["$RUA"]['close'][0]*min_ratio, \
all_data["$RUA"]['close'][0]*max_ratio])
dg.set_axis_options(axis='y2', valueRange=[all_data["AAPL"]['close'][0]*min_ratio, \
all_data["AAPL"]['close'][0]*max_ratio])
dg.annotate('AAPL', '2010-06-21', "B", "Buy on 2010-06-21")
dg.annotate('AAPL', '2010-08-13', "S", "Sell on 2010-08-13")
dg.set_options(title="Tutorial 4", ylabel="Russel 3000", y2label="AAPL")
div = dg.savefig(csv_file="tutorial4.csv", html_file="tutorial4.html")
import datetime as dt
from finpy.financial.equity import get_tickdata
import finpy.utils.fpdateutil as du
from finpy.financial.portfolio import Portfolio
from dyplot.dygraphs import Dygraphs
if __name__ == '__main__':
dt_timeofday = dt.timedelta(hours=16)
dt_start = dt.datetime(2014, 9, 1)
dt_end = dt.datetime(2014, 12, 31)
ls_symbols = ['AAPL']
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_stocks = get_tickdata(ls_symbols=ls_symbols, ldt_timestamps=ldt_timestamps)
p = Portfolio(all_stocks, 0, ldt_timestamps)
p.normalized_price(tick="AAPL")
dg = Dygraphs(ldt_timestamps, "date")
dg.candleplot(open=p.equities["AAPL"]['open'],
high=p.equities["AAPL"]['high'],
low=p.equities["AAPL"]['low'],
close=p.equities["AAPL"]['close'])
dg.set_options(title="Tutorial 7")
div = dg.savefig(csv_file="tutorial7.csv", html_file="tutorial7.html")
import datetime as dt
from finpy.financial.equity import get_tickdata
import finpy.utils.fpdateutil as du
from finpy.financial.portfolio import Portfolio
from dyplot.dygraphs import Dygraphs
if __name__ == '__main__':
dt_timeofday = dt.timedelta(hours=16)
dt_start = dt.datetime(2014, 9, 1)
dt_end = dt.datetime(2014, 12, 31)
ls_symbols = ['AAPL']
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_stocks = get_tickdata(ls_symbols=ls_symbols, ldt_timestamps=ldt_timestamps, source="Google")
p = Portfolio(all_stocks, 0, ldt_timestamps)
dg = Dygraphs(ldt_timestamps, "date")
dg.candleplot(open=p.equities["AAPL"]['open'],
high=p.equities["AAPL"]['high'],
low=p.equities["AAPL"]['low'],
close=p.equities["AAPL"]['close'])
dg.plot(series="10D MA", mseries=p.moving_average(window=20, tick="AAPL"))
dg.set_options(title="Tutorial 8")
div = dg.savefig(csv_file="tutorial8.csv", html_file="tutorial8.html")
def algo_wrapper(self, tick):
""" The wrapper for algo(). Generate various data for backtesting and viewing.
:param tick: The ticker of the security.
:return tick: The ticker of the security.
:return return_ratio: The return ratio of the algorithm.
:return stock_return: The return ratio of the security in the same preriod.
:return pf.order: The order of t
"""
dt_timeofday = dt.timedelta(hours=16)
equities = get_tickdata(ls_symbols=[tick], ldt_timestamps=self.ldt_timestamps)
pf, stat = self.algo(equities=equities, tick=tick)
# Prepare Data for vaiour charts and diagrams
csvfile = os.path.join(self.args.dir, 'static', 'csv', self.args.subdir,tick + '.csv')
pf.csvwriter(equity_col=["shares", "close"], csv_file=csvfile, total=True, cash=True, d=',')
csvfile = os.path.join(self.args.dir, 'static', 'csv', self.args.subdir,tick + '_order.csv')
pf.write_order_csv(csv_file=csvfile)
stdev = pf.rolling_normalized_stdev(tick)
market_nml = self.benchmark.normalized(self.benchmark_tick)
market_dygraph = ";" + market_nml.map(str) + ";"
tick_nml = pf.normalized(tick)
total_nml = pf.total/pf.total[0]
algo = tick + ' ALGO'
ba = pf.bollinger_band(tick)
ba_hi_nml = ba['hi']/pf.equities[tick]['close'][0]
ba_lo_nml = ba['lo']/pf.equities[tick]['close'][0]
col = ['b', 'r', 'g', 'c', 'y', 'm']
buy_list = [x.date for x in pf.order if x.action == "buy"]
buy_price = pf.normalized(tick)[buy_list]
sell_list = [x.date for x in pf.order if x.action == "sell"]
sell_price = pf.normalized(tick)[sell_list]
dg = Dygraphs(tick_nml.index, "date")
dg.plot(series=tick, mseries = tick_nml, lseries = ba_lo_nml, hseries = ba_hi_nml)
dg.plot(series=algo, mseries = total_nml)
dg.plot(series=self.benchmark_tick, mseries = market_nml)
for o in pf.order:
text = tick + '@' + str(pf.equities[tick]['close'][o.date]) + ' on ' \
+ o.date.strftime("%Y-%m-%d")
dg.annotate(tick, o.date.strftime("%Y-%m-%d"), o.action[0].upper(), text)
csv_file = os.path.join(self.args.dir, 'static', 'csv', self.args.subdir, tick + "_dygraph.csv")
csv_url = "/static/csv/" + self.args.subdir + "/" + tick + "_dygraph.csv"
div_id = "id0"
js_vid = 'dyg'
dg.set_options(title=tick, ylabel="Ratio")
dg.set_axis_options("x", drawAxis = False)
div = dg.savefig(csv_file=csv_file, div_id=div_id, js_vid=js_vid, dt_fmt="%Y-%m-%d", csv_url=csv_url)
gstd = Dygraphs(self.ldt_timestamps, "date")
gstd.plot(series=tick, mseries= stdev)
csv_file = os.path.join(self.args.dir, 'static', 'csv', self.args.subdir, tick + "_std.csv")
csv_url = "/static/csv/" + self.args.subdir + "/" + tick + "_std.csv"
div_id = "id1"
js_vid = 'std'
gstd.set_options(ylabel="Volatility", digitsAfterDecimal="4", height=120)
divstd = gstd.savefig(csv_file=csv_file, div_id=div_id, js_vid=js_vid, dt_fmt="%Y-%m-%d", height="100ptx", csv_url=csv_url)
fail = 0
succ = 0
pie = ""
for x in stat:
if x.sell_date != None:
if x.sell_price > x.buy_price:
succ += 1
else:
fail += 1
if succ+fail != 0:
succ_frac = succ*100/(succ+fail)
fail_frac = fail*100/(succ+fail)
labels = 'Success', 'Fail'
gpie = Pie([succ_frac, fail_frac], labels=labels)
div_id = tick + "_pie"
js_vid = 'pie' + tick
pie = gpie.savefig(div_id=div_id, js_vid=js_vid)
return_ratio = pf.return_ratio()
stock_return = pf.equities[tick]['close'][-1]/pf.equities[tick]['close'][0]
return (tick, return_ratio, stock_return, pf.order, equities[tick], stat, div, divstd, pie)
def backtesting(self):
market = self.benchmark
buy_price = {}
output = '<table class="table table-bordered">\n'
output += "<th>Date</th><th>Action</th><th>Ticker</th><th>Shares</th>"
output += "<th>Price</th><th>Cash Before</th><th>Cash After</th><th>Gain</th>"
for o in self.all_order:
self.pf.cal_total(o.date)
sdate = o.date.strftime("%Y-%m-%d")
if o.action == "buy":
output += "<tr>\n"
if self.pf.cash[o.date] >= 200000.0:
buy_size = 20000.0
elif self.pf.cash[o.date] >= 100000.0:
buy_size = 10000.0
else:
buy_size = 5000.0
if self.pf.cash[o.date] >= buy_size:
shares = np.floor(buy_size/o.price)
print("buy", shares, "of", o.tick, "at", o.price, o.date)
ticka = '<a href=\"%s.html\">%s</a>' %(o.tick, o.tick)
output += "<td>%s</td><td>buy</td><td>%s</td><td>%d</td><td>%f</td>" %(sdate,ticka,shares,o.price)
output += "<td>%d</td>" %(self.pf.cash[o.date])
self.pf.buy(shares=shares, tick=o.tick, price=o.price, date=o.date, update_ol=True)
output += "<td>%d</td>" %(self.pf.cash[o.date])
output += "<td></td>\n"
buy_price[o.tick] = o.price
else:
shares = np.floor(buy_size/o.price)
print("Not enough cash. Skip buying", shares, "of", o.tick, "at", o.price, "on", o.date)
ticka = '<a href=\"%s.html\">%s</a>' %(o.tick, o.tick)
output += "<td>%s</td><td>skip buy</td><td>%s</td><td>%d</td><td>%f</td>\n" %(sdate,ticka,shares,o.price)
output += "<td></td><td></td><td></td>\n"
else:
shares = self.pf.equities[o.tick]['shares'][o.date]
price = self.pf.equities[o.tick]['close'][o.date]
if shares > 0:
print("sell", shares, " of", o.tick, " at", o.price, o.date, ". A gain of", o.price/buy_price[o.tick])
gain = o.price/buy_price[o.tick]
if gain > 1:
output += "<tr class=\"success\">\n"
else:
output += "<tr class=\"danger\">\n"
ticka = '<a href=\"%s.html\">%s</a>' %(o.tick, o.tick)
output += "<td>%s</td><td>sell</td><td>%s</td><td>%d</td><td>%f</td>" %(sdate,ticka,shares,o.price)
output += "<td>%d</td>" %(self.pf.cash[o.date])
self.pf.sell(date=o.date, tick=o.tick, shares=shares, price=price, update_ol=True)
output += "<td>%d</td>\n" %(self.pf.cash[o.date])
output += "<td>%f</td>\n" %(gain)
output += "</tr>\n"
output += "</table>\n"
fund_csv = os.path.join(self.args.dir, 'static', 'csv', self.args.subdir,'fund.csv')
self.pf.csvwriter(equity_col=["shares"], csv_file=fund_csv, total=True, cash=True, d=',')
fund_order_csv = os.path.join(self.args.dir, 'static', 'csv', self.args.subdir,'fund_order.csv')
self.pf.write_order_csv(csv_file=fund_order_csv)
ldt_timestamps = self.pf.ldt_timestamps()
self.pf.cal_total(ldt_timestamps[-1])
dg = Dygraphs(ldt_timestamps, "date")
dg.plot(series='Market', mseries = market.normalized('$RUA'))
dg.plot(series="Portfolio", mseries = self.pf.total/self.pf.total[0])
csv_file = os.path.join(self.args.dir, 'static', 'csv', self.args.subdir,'fund_dygraph.csv')
div_id = "fund_graphdiv"
js_vid = 'gfund'
dg.set_options(title="Fund Performance")
csv_url = "/static/csv/" + self.args.subdir + "/" + "fund_dygraph.csv"
fund_graph = dg.savefig(csv_file=csv_file, div_id=div_id, js_vid=js_vid, dt_fmt="%Y-%m-%d", csv_url=csv_url)
return output, fund_graph
date_list = [x.date for x in order_list]
date_list.sort()
dt_start = date_list[0]
dt_end = date_list[-1]
tick_set = set([x.tick for x in order_list])
ls_symbols = ['$SPX']
while(tick_set):
ls_symbols.append(tick_set.pop())
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_stocks = get_tickdata(ls_symbols=ls_symbols, ldt_timestamps=ldt_timestamps)
pf = Portfolio(equities=all_stocks, cash=cash, dates=ldt_timestamps, order_list=order_list)
pf.sim()
equity_col = ['buy', 'sell', 'close']
pf.csvwriter(csv_file=value_file, d=',', cash=False)
print("Details of the Performance of the portfolio :")
print("Data Range :", ldt_timestamps[0], "to", ldt_timestamps[-1])
print("Sharpe Ratio of Fund :", pf.sharpe_ratio())
print("Sharpe Ratio of $SPX :", pf.sharpe_ratio('$SPX'))
print("Total Return of Fund :", pf.return_ratio())
print(" Total Return of $SPX :", pf.return_ratio('$SPX'))
print("Standard Deviation of Fund :", pf.std())
print(" Standard Deviation of $SPX :", pf.std('$SPX'))
print("Average Daily Return of Fund :", pf.avg_daily_return())
print("Average Daily Return of $SPX :", pf.avg_daily_return('$SPX'))
dg = Dygraphs(ldt_timestamps, "date")
dg.plot(series='S&P 500', mseries=pf.normalized('$SPX'))
dg.plot(series='Return', mseries=pf.normalized())
dg.set_options(title="Market Sim")
dg.savefig(csv_file="marketsim.csv", html_file="marketsim.html")
import datetime as dt
from finpy.financial.equity import get_tickdata
import finpy.utils.fpdateutil as du
from finpy.financial.portfolio import Portfolio
from dyplot.dygraphs import Dygraphs
if __name__ == '__main__':
dt_timeofday = dt.timedelta(hours=16)
dt_start = dt.datetime(2010, 1, 1)
dt_end = dt.datetime(2010, 12, 31)
ls_symbols = ['AAPL','XOM', 'MSFT', 'WMT']
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_data = get_tickdata(ls_symbols=ls_symbols, ldt_timestamps=ldt_timestamps)
all_stocks = Portfolio(all_data, 0, ldt_timestamps, [])
dg = Dygraphs(ldt_timestamps, "date")
for tick in ls_symbols:
dg.plot(series=tick, mseries=all_stocks.normalized(tick))
dg.set_options(title="Tutorial 2")
div = dg.savefig(csv_file="tutorial2.csv", html_file="tutorial2.html")
import datetime as dt
from finpy.financial.equity import get_tickdata
import finpy.utils.fpdateutil as du
from finpy.financial.portfolio import Portfolio
from dyplot.dygraphs import Dygraphs
if __name__ == '__main__':
dt_timeofday = dt.timedelta(hours=16)
dt_start = dt.datetime(2014, 9, 1)
dt_end = dt.datetime(2014, 12, 31)
ls_symbols = ['AAPL']
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_stocks = get_tickdata(ls_symbols=ls_symbols,
ldt_timestamps=ldt_timestamps,
source="Google")
p = Portfolio(all_stocks, 0, ldt_timestamps)
dg = Dygraphs(ldt_timestamps, "date")
dg.candleplot(open=p.equities["AAPL"]['open'],
high=p.equities["AAPL"]['high'],
low=p.equities["AAPL"]['low'],
close=p.equities["AAPL"]['close'])
dg.plot(series="10D MA", mseries=p.moving_average(window=20, tick="AAPL"))
dg.set_options(title="Tutorial 8")
div = dg.savefig(csv_file="tutorial8.csv", html_file="tutorial8.html")
import datetime as dt
from finpy.financial.equity import get_tickdata
import finpy.utils.fpdateutil as du
from finpy.financial.portfolio import Portfolio
from dyplot.dygraphs import Dygraphs
if __name__ == '__main__':
dt_timeofday = dt.timedelta(hours=16)
dt_start = dt.datetime(2014, 9, 1)
dt_end = dt.datetime(2014, 12, 31)
ls_symbols = ['AAPL']
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_stocks = get_tickdata(ls_symbols=ls_symbols,
ldt_timestamps=ldt_timestamps)
p = Portfolio(all_stocks, 0, ldt_timestamps)
p.normalized_price(tick="AAPL")
dg = Dygraphs(ldt_timestamps, "date")
dg.candleplot(open=p.equities["AAPL"]['open'],
high=p.equities["AAPL"]['high'],
low=p.equities["AAPL"]['low'],
close=p.equities["AAPL"]['close'])
dg.set_options(title="Tutorial 7")
div = dg.savefig(csv_file="tutorial7.csv", html_file="tutorial7.html")
import datetime as dt
import numpy as np
import pandas as pd
from dyplot.dygraphs import Dygraphs
if __name__ == '__main__':
foo = np.random.rand(100)
boo = pd.Series(foo, index=range(0, 100))
dg = Dygraphs(boo.index, "date")
dg.plot(series="Random", mseries=boo)
dg.set_options(title="Tutorial 0")
div = dg.savefig(csv_file="tutorial0.csv",
div_id="demodiv",
js_vid="jid",
html_file="tutorial0.html")
print(div)
import datetime as dt
from finpy.financial.equity import get_tickdata
import finpy.utils.fpdateutil as du
from finpy.financial.portfolio import Portfolio
from dyplot.dygraphs import Dygraphs
if __name__ == '__main__':
dt_timeofday = dt.timedelta(hours=16)
dt_start = dt.datetime(2010, 1, 1)
dt_end = dt.datetime(2010, 12, 31)
ls_symbols = ['AAPL', 'XOM', 'MSFT', 'WMT']
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_data = get_tickdata(ls_symbols=ls_symbols,
ldt_timestamps=ldt_timestamps)
all_stocks = Portfolio(all_data, 0, ldt_timestamps, [])
dg = Dygraphs(ldt_timestamps, "date")
for tick in ls_symbols:
dg.plot(series=tick, mseries=all_stocks.normalized(tick))
dg.set_options(title="Tutorial 2")
div = dg.savefig(csv_file="tutorial2.csv", html_file="tutorial2.html")
import datetime as dt
from finpy.financial.equity import get_tickdata
import finpy.utils.fpdateutil as du
from dyplot.dygraphs import Dygraphs
if __name__ == '__main__':
dt_timeofday = dt.timedelta(hours=16)
dt_start = dt.datetime(2010, 1, 1)
dt_end = dt.datetime(2010, 12, 31)
ls_symbols = ['AAPL','$RUA']
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_stocks = get_tickdata(ls_symbols=ls_symbols, ldt_timestamps=ldt_timestamps)
dg = Dygraphs(ldt_timestamps, "date")
dg.plot(series="AAPL", mseries=all_stocks["AAPL"]['close'], axis='y2')
dg.plot(series="$RUA", mseries=all_stocks["$RUA"]['close'])
dg.set_options(title="Tutorial 3")
div = dg.savefig(csv_file="tutorial3.csv", html_file="tutorial3.html")