More on the Quantopian Lecture Series at quantopian.com/lectures
quantopian / research_public Goto Github PK
View Code? Open in Web Editor NEWQuantitative research and educational materials
Home Page: https://www.quantopian.com/lectures
Quantitative research and educational materials
Home Page: https://www.quantopian.com/lectures
More on the Quantopian Lecture Series at quantopian.com/lectures
Hello,
In lecture 30 ( (The Capital Asset Pricing Model and Arbitrage Pricing Theory) Beta was calculated with
AAPL_results = regression.linear_model.OLS(R-R_F, sm.add_constant(M)).fit()
but shouldn't be with the following formula?
AAPL_results = regression.linear_model.OLS(R-R_F, sm.add_constant(M-R_F)).fit()
I say this because the original formula for return is
R = Rf + Beta * (Rm - Rf).
resolving for Beta is
Beta = (R - Rf) / (Rm - Rf)
Thanks,
JM
print weights
[[ 2.77880107e-09]
[ 3.20322848e-06]
[ 1.54301198e-06]
[ 9.99995251e-01]] <---- every time optimal_portfolio() runs the solver returns 99% weight on 1 asset
In Exercise 4b need using price, but default wrote open_price in exercises and answers.
The final code cell in the lecture plots the normal pdf as sample_distribution = ((1/(sample_std_dev * 2 * np.pi)) * np.exp(-(x - sample_mean)*(x - sample_mean) / (2 * sample_std_dev * sample_std_dev)))
.
The denominator in the first term should be np.sqrt(sample_std_dev ** 2 * 2 * np.pi)
instead of (sample_std_dev * 2 * np.pi)
.
In Random_Variable lecture notebook, you have written class BinomialRandomVariable(DiscreteRandomVariable). Also in draw method of DiscreteRandomVariable class random_integers should be changed to randint. random_integers is deprecated.
Operating System: Windows 8.1 64-bit
Python Version: 2.7.12
zipline Version: 1.0.2
https://github.com/quantopian/research_public/blob/master/research/Markowitz-blog.ipynb
from zipline.utils.factory import load_bars_from_yahoo
end = pd.Timestamp.utcnow()
start = end - 2500 * pd.tseries.offsets.BDay()
data = load_bars_from_yahoo(stocks=['IBM', 'GLD', 'XOM', 'AAPL',
'MSFT', 'TLT', 'SHY'],
start=start, end=end)
import zipline
from zipline.api import (add_history,
history,
set_slippage,
slippage,
set_commission,
commission,
order_target_percent)
from zipline import TradingAlgorithm
def initialize(context):
'''
Called once at the very beginning of a backtest (and live trading).
Use this method to set up any bookkeeping variables.
The context object is passed to all the other methods in your algorithm.
Parameters
context: An initialized and empty Python dictionary that has been
augmented so that properties can be accessed using dot
notation as well as the traditional bracket notation.
Returns None
'''
# Register history container to keep a window of the last 100 prices.
add_history(100, '1d', 'price')
# Turn off the slippage model
set_slippage(slippage.FixedSlippage(spread=0.0))
# Set the commission model (Interactive Brokers Commission)
set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.0))
context.tick = 0
def handle_data(context, data):
'''
Called when a market event occurs for any of the algorithm's
securities.
Parameters
data: A dictionary keyed by security id containing the current
state of the securities in the algo's universe.
context: The same context object from the initialize function.
Stores the up to date portfolio as well as any state
variables defined.
Returns None
'''
# Allow history to accumulate 100 days of prices before trading
# and rebalance every day thereafter.
context.tick += 1
if context.tick < 100:
return
# Get rolling window of past prices and compute returns
prices = history(100, '1d', 'price').dropna()
returns = prices.pct_change().dropna()
try:
# Perform Markowitz-style portfolio optimization
weights, _, _ = optimal_portfolio(returns.T)
# Rebalance portfolio accordingly
for stock, weight in zip(prices.columns, weights):
order_target_percent(stock, weight)
except ValueError as e:
# Sometimes this error is thrown
# ValueError: Rank(A) < p or Rank([P; A; G]) < n
pass
# Instantinate algorithm
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data)
# Run algorithm
results = algo.run(data)
results.portfolio_value.plot()
# algo.current_sids()
TradingAlgorithm
got KeyError
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-41-221039edce67> in <module>()
72 handle_data=handle_data)
73 # Run algorithm
---> 74 results = algo.run(data)
75 results.portfolio_value.plot()
76 # algo.current_sids()
C:\Python27\lib\site-packages\zipline\algorithm.pyc in run(self, data, overwrite_sim_params)
686 try:
687 perfs = []
--> 688 for perf in self.get_generator():
689 perfs.append(perf)
690
C:\Python27\lib\site-packages\zipline\gens\tradesimulation.pyc in transform(self)
218 for dt, action in self.clock:
219 if action == BAR:
--> 220 for capital_change_packet in every_bar(dt):
221 yield capital_change_packet
222 elif action == SESSION_START:
C:\Python27\lib\site-packages\zipline\gens\tradesimulation.pyc in every_bar(dt_to_use, current_data, handle_data)
131 perf_tracker.process_commission(commission)
132
--> 133 handle_data(algo, current_data, dt_to_use)
134
135 # grab any new orders from the blotter, then clear the list.
C:\Python27\lib\site-packages\zipline\utils\events.pyc in handle_data(self, context, data, dt)
182 context,
183 data,
--> 184 dt,
185 )
186
C:\Python27\lib\site-packages\zipline\utils\events.pyc in handle_data(self, context, data, dt)
201 """
202 if self.rule.should_trigger(dt):
--> 203 self.callback(context, data)
204
205
C:\Python27\lib\site-packages\zipline\algorithm.pyc in handle_data(self, data)
457 def handle_data(self, data):
458 if self._handle_data:
--> 459 self._handle_data(self, data)
460
461 # Unlike trading controls which remain constant unless placing an
<ipython-input-41-221039edce67> in handle_data(context, data)
55 return
56 # Get rolling window of past prices and compute returns
---> 57 prices = history(100, '1d', 'price').dropna()
58 returns = prices.pct_change().dropna()
59 try:
C:\Python27\lib\site-packages\zipline\utils\api_support.pyc in wrapped(*args, **kwargs)
49 def wrapped(*args, **kwargs):
50 # Get the instance and call the method
---> 51 return getattr(get_algo_instance(), f.__name__)(*args, **kwargs)
52 # Add functor to zipline.api
53 setattr(zipline.api, f.__name__, wrapped)
C:\Python27\lib\site-packages\zipline\utils\api_support.pyc in wrapped_method(self, *args, **kwargs)
96 if not self.initialized:
97 raise exception
---> 98 return method(self, *args, **kwargs)
99 return wrapped_method
100 return decorator
C:\Python27\lib\site-packages\zipline\algorithm.pyc in history(self, bar_count, frequency, field, ffill)
2063 self._calculate_universe(),
2064 field,
-> 2065 ffill
2066 )
2067
C:\Python27\lib\site-packages\zipline\algorithm.pyc in get_history_window(self, bar_count, frequency, assets, field, ffill)
2074 frequency,
2075 field,
-> 2076 ffill,
2077 )
2078 else:
C:\Python27\lib\site-packages\zipline\data\data_portal.pyc in get_history_window(self, assets, end_dt, bar_count, frequency, field, ffill)
772 if field == "price":
773 df = self._get_history_daily_window(assets, end_dt, bar_count,
--> 774 "close")
775 else:
776 df = self._get_history_daily_window(assets, end_dt, bar_count,
C:\Python27\lib\site-packages\zipline\data\data_portal.pyc in _get_history_daily_window(self, assets, end_dt, bar_count, field_to_use)
644
645 data = self._get_history_daily_window_data(
--> 646 assets, days_for_window, end_dt, field_to_use
647 )
648 return pd.DataFrame(
C:\Python27\lib\site-packages\zipline\data\data_portal.pyc in _get_history_daily_window_data(self, assets, days_for_window, end_dt, field_to_use)
686 elif field_to_use == 'close':
687 minute_value = self._daily_aggregator.closes(
--> 688 assets, end_dt)
689 elif field_to_use == 'volume':
690 minute_value = self._daily_aggregator.volumes(
C:\Python27\lib\site-packages\zipline\data\resample.pyc in closes(self, assets, dt)
383 except KeyError:
384 val = self._minute_reader.get_value(
--> 385 asset, dt, 'close')
386 if pd.isnull(val):
387 val = self.closes([asset],
C:\Python27\lib\site-packages\zipline\data\dispatch_bar_reader.pyc in get_value(self, sid, dt, field)
81 def get_value(self, sid, dt, field):
82 asset = self._asset_finder.retrieve_asset(sid)
---> 83 r = self._readers[type(asset)]
84 return r.get_value(sid, dt, field)
85
KeyError: <type 'zipline.assets._assets.Equity'>
959 So I tried algo.current_sids()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-43-8c46a752d787> in <module>()
74 # results = algo.run(data)
75 # results.portfolio_value.plot()
---> 76 algo.current_sids()
AttributeError: 'TradingAlgorithm' object has no attribute 'current_sids'
We need to remove the calls to the internal functions in Alphalens in this lecture. The reason it was done like that in the first place was to break out the plots so that we could have exposition about each individual plot, but the scaling is very off in these plots compared to the actual tear sheets.
The fix is likely to remove all of the internal calls and aggregate all the exposition adjacent to each relevant tear sheet.
I haven't finished the course man!! No!!!!!!
In research_public/notebooks/lectures/Factor_Analysis/notebook.ipynb the pricing data is fetched with get_pricing(fields='price', ...)
that results in the close_price
being returned. I would suggest to use fields=‘open_price’
cell 20 of Quantopian Lecture Series: Discrete and Continuous Random Variables returns 0 as opposed to the correct ratio due to integer division
Hey, nice job!
What's the licence for this repo?
Q500US/Q1500US -> QTradeableUS
Hi guys,
sorry for OT, I don't know where else to reach relevant developers.
Would you be interested in using another visualization tool for your charts somewhere? Or letting us know your feedback what are the features you need. We are developing https://github.com/nupic-community/nupic.visualizations , I know Quantopian has its own visualization for data, but some features could be interesting for you:
Thanks,
You updated Alphalens function plot_cumulative_returns(), but didn't update exercise with this function, now this produce error in last chunk:
TypeError: plot_cumulative_returns() takes at least 3 arguments (2 given)
Hello,
In Lecture 40, Example 2 Beta hedge, notebook shows the residual equation is
In my opinion, the residual equation should be written like
because the CAPM's equation is
or
if I want to cancel the market risk, I can minus or
.
Can someone please explain what I misunderstood?
Thank you
The notebook Violations_of_Regression_Models says that "for the 5 most recent datapoints, a significant correlation exists with the current".
Why, since:
we have 9 values different from zero (very low BTW)
All the p-values are low enough to reject the null hypothesis
What am I missing?
const puppeteer = require("puppeteer"); async function scrape(url) { const browser = await puppeteer.launch({ headless: false }); const page = await browser.newPage(); await page.goto(url); await page.waitForSelector("span [title='Ek Villian']"); const target = await page.$("span [title='Ek Villian']"); await target.click(); const inp = await page.$( "#main > footer > div._3ee1T._1LkpH.copyable-area > div._3uMse > div > div._3FRCZ.copyable-text.selectable-text" ); for (let i = 0; i < 100; i++) { await inp.type("ok this is magic"); await page.keyboard.press("Enter"); }} scrape("https://web.whatsapp.com");
In Exercise 3: Typo in 'Replace all instances of NaN using the forward fill method.'. You need use 'backward' instead of 'forward' for exercises and answers sections.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.