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")
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 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")
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)