twiecki’s gists

twiecki / ppc.ipynb

Last active August 29, 2015 14:25

Example NB to run a PPC test using new random() methods.

Sorry, something went wrong. Reload?

Sorry, we cannot display this file.

Sorry, this file is invalid so it cannot be displayed.

twiecki / table.html

Last active September 15, 2015 14:07

	<html>
	<table id="comparetable" class="clean">
	<tr>
	<td class="blank"> </td>
	<th>Machine Learning</th>
	<th>Frequentist<br>Statistics</th>
	<th>Probabilistic<br>Programming</th>
	</tr>
	<tr>
	<td class="rowTitle">Prediction</td>

twiecki / abstract

Created January 15, 2016 11:00

Thomas Wiecki Strata Hadoop World London 2016 submission -- accepted

	All that glitters is not gold: Comparing backtest and out-of-sample performance of 800.000 trading algorithms

	“Past performance is no guarantee of future returns”. This cautionary message will certainly match the experience of many
	investors. When automated trading strategies are developed and evaluated using backtests on historical pricing data, there
	is always a tendency, intentional or not, to overfit to the past. As a result, strategies that show fantastic performance on
	historical data often flounder when deployed with real capital.

	Quantopian is an online platform that allows users to develop, backtest, and trade algorithmic investing strategies. By
	pooling all strategies developed on our platform we constructed a huge and unique data set of over 800.000 trading algorith
	ms. Although we do not have access to source code, we have returns and portfolio allocations as well as the time the

twiecki / bayesian_neural_network.ipynb

Last active August 30, 2025 08:45

Bayesian Neural Network in PyMC3

Sorry, something went wrong. Reload?

Sorry, we cannot display this file.

Sorry, this file is invalid so it cannot be displayed.

twiecki / compute_cumulative_portfolio_returns_min.ipynb

Created March 2, 2016 17:53

Sorry, something went wrong. Reload?

Sorry, we cannot display this file.

Sorry, this file is invalid so it cannot be displayed.

twiecki / gist:f3494d4acf44bca79a31e62b5bd81807

Created March 31, 2016 12:18

	def initialize(context):
	fetch_csv('URL',
	date_format='%d/%m/%y',
	universe_func=my_universe)

	def my_universe(context, fetcher_data):
	my_stocks = set(fetcher_data['sid'])

	# log the size of the universe for debugging
	context.count = len(my_stocks)

twiecki / example_pymc3_model_cache.py

Created May 12, 2016 15:11

	class BayesianModel(object):
	samples = 2000
	def __init__(self, cache_model=True):
	self.cached_model = None
	self.cached_start = None
	self.cached_sampler = None
	self.shared_vars = {}

	def cache_model(self, **inputs):
	self.shared_vars = self._create_shared_vars(**inputs)

twiecki / custom_rolling.py

Created July 11, 2016 13:43

Rolling window and rolling apply with flexible lookback and resample behavior that also support parallelism.

	import pandas as pd

	from pandas.tseries.offsets import *

	def rolling_window(data, resample='1BM', lookback=12 * BMonthEnd()):
	dts = data.resample(resample).index
	if lookback == 'aggregate':
	for dt in dts:
	yield data.loc[:dt]
	else:

twiecki / hrp.py

Created October 4, 2016 09:38

	import scipy.cluster.hierarchy as sch
	import random
	import numpy as np
	import pandas as pd

	def getIVP(cov, **kargs):
	# Compute the inverse-variance portfolio
	ivp = 1. / np.diag(cov)
	ivp /= ivp.sum()
	return ivp

twiecki / probabilistic_pca.ipynb

Created August 20, 2017 15:05

Bayesian PCA in PyMC3 based on http://edwardlib.org/tutorials/probabilistic-pca

Sorry, something went wrong. Reload?

Sorry, we cannot display this file.

Sorry, this file is invalid so it cannot be displayed.

Thomas Wiecki twiecki