Quantitative Finance.txt

###########
### To Do

# develop flipped classroom model
I'm very interested in converting some of my materials to an online, interactive tutorial for the purpose of developing a flipped classroom model.  Below is a link to my lecture materials. 
The PDF files contain the narration text, formulas, and R code. Most slides have narration and formulas on the left, and the corresponding R code on the right.
For example you can take a look at time_series_multivariate.pdf
I also provide .R files containing the code, so that students can execute the code as they listen to the lecture. The lecture materials also contain .RData files with sample data. 
https://drive.google.com/open?id=0Bxzva1I0t63vVGEtaXNIY1JMa00

# contribute to quant-lab
https://r-forge.r-project.org/projects/quant-lab/

# create website
https://wp.nyu.edu/

# get certification from AcademyR
http://www.revolutionanalytics.com/academyr-training-education

# Regression Models course
https://class.coursera.org/regmods-004/wiki/Lecture_note_links
https://github.com/algoquant/courses
http://www.rpubs.com/santam/logistic-regression

# write gitbook
https://www.gitbook.io/explore

# answer questions:
http://quant.stackexchange.com/questions/11413/how-to-compute-daily-skewness-of-sp-daily-return-timeseries-under-no-other-more
http://stats.stackexchange.com/questions/97783/understanding-vec2var-conversion-in-r
# needs an answer
http://quant.stackexchange.com/questions/9773/ib-with-r-which-package



###########
### quant top people resources

# Brian Peterson and Joshua Ulrich
Brian Peterson: Quantitative Strategy Development
https://r-forge.r-project.org/scm/viewvc.php/*checkout*/pkg/quantstrat/sandbox/backtest_musings/strat_dev_process.pdf?root=blotter
https://www.youtube.com/channel/UC0ZtUJOGGei4w5lxA_p8YKg
packages quantstrat, TTR, PerformanceAnalytics
Jeff Ryan packages xts, quantmod
Matt Dowle package data.table

# Aleksey Zemnitskiy Snowfall Systems PortfolioEffect

# top kaggle machine learning packages: 
glmnet for Lasso and elastic-net regularized generalized linear models
gbm for gradient boosted ensemble models
e1071 for support vector machines C++ library libsvm

# Benjamin E. Van Vliet at IIT Chicago
http://stuart.iit.edu/faculty/benjamin-van-vliet



###########
### quant other people resources

# Attilio Meucci - KKR, SYMMYS, KEPOS
http://www.symmys.com

# The Thalesians
http://www.thalesians.com/finance/index.php/Main_Page

http://cran.stat.ucla.edu/web/views/Finance.html

http://matlab-trading.blogspot.ca
http://tradingwithpython.blogspot.com
http://www.statsblogs.com/tag/asset-allocation

http://quantivity.wordpress.com

# Empirical Finance
http://cran.r-project.org/web/views/Finance.html

# David Easley at Cornell: VPIN metric, volume clock, high frequency trading, machine learning
http://infosci.cornell.edu/faculty/david-easley



###########
### academics associations

# academic research websites top
http://blog.alphaarchitect.com/2015/04/01/where-to-find-cool-academic-finance-research/

# Q Group - Institute for Quantitative Research
http://www.q-group.org/
# Jack Treynor Prize Winners
http://www.q-group.org/jack-treynor-prize-winners/

# Real World Risk Institute by Nassim Taleb and Robert J. Frey (from Renaissance Technologies)
http://realworldrisk.com/

# Thalesians by Paul Bilokon at Deutsche Bank and Matthew Dixon in NYC 
http://www.thalesians.com/finance/index.php/Main_Page

# Francis Diebold
http://fxdiebold.blogspot.com/

# Christopher Krauss Statistical Arbitrage Pairs Trading market anomalies momentum strategies value strategies
http://www.statistik.wiso.uni-erlangen.de/lehrstuhl/mitarbeiter/krauss.shtml

# Tim Leung University of Washington Pairs Trading
https://sites.google.com/site/timleungresearch/

# Thierry Roncalli - Kalman filter
http://www.thierry-roncalli.com/

# Andrea Frazzini - AQR & NYU
http://www.econ.yale.edu/~af227/
http://pages.stern.nyu.edu/~sternfin/afrazzini/index.htm
http://www.stern.nyu.edu/faculty/bio/andrea-frazzini

# Zura Kakushadze Quantigic Solutions
http://www.quantigic.com

# Nassim Nicholas Taleb
http://nassimtaleb.org/
http://www.fooledbyrandomness.com/

# Drew Creal Time-Varying Parameter Time Series GAS Models
http://faculty.chicagobooth.edu/drew.creal/

# Campbell R. Harvey backtesting data mining bonferroni adjustment
http://www.fuqua.duke.edu/faculty_research/faculty_directory/harvey/

# Wayne Ferson investment performance evaluation
http://www-bcf.usc.edu/~ferson/

# John H. Cochrane - Chicago Booth & AQR
http://faculty.chicagobooth.edu/john.cochrane/
# excellent courses, but no concise notes
http://faculty.chicagobooth.edu/john.cochrane/teaching/index.htm
# Notes for Time Series for Macroeconomics and Finance
http://faculty.chicagobooth.edu/john.cochrane/research/papers/time_series_book.pdf
# Asset Pricing
https://www.coursera.org/course/assetpricing

# Andrew Ang factors, anomalies
http://www.columbia.edu/~aa610/index.html

# Bruce E. Hansen - free econometrics manuscript
http://www.ssc.wisc.edu/~bhansen/
http://www.ssc.wisc.edu/~bhansen/crete/

# Ludwig Chincarini book Quantitative Equity Portfolio Management
http://www.usfca.edu/Faculty/Management/Ludwig_Chincarini/

# Matt Brigida teaching resources using R/Shiny
matt@complete-markets.com
http://complete-markets.com/sample-page/
https://github.com/Matt-Brigida

# Meese Rogoff Currency Forecasting Paper
http://www.ssc.wisc.edu/~bhansen/390/MeeseRogoff.pdf

# Robert Almgren - friend of Neil Chriss - Quantitative Brokers, high frequency volatility, order book
http://quantitativebrokers.com/
http://www.courant.nyu.edu/~almgren/

# Petter Kolm Director of Mathematics in Finance Program at NYU Courant
# teaches Algorithmic Trading and Quantitative Strategies
petter.kolm@nyu.edu
http://www.cims.nyu.edu/~kolm/
http://www.thestreet.com/story/10395354/1/how-nyu-plans-to-create-the-super-quant.html

# Peter Phillips (Yale professor) - unit root tests
http://korora.econ.yale.edu/phillips

# Alvaro Cartea - high frequency trading
http://www.cartea.net/
https://sites.google.com/site/alvarocartea/home/working_papers
http://scholar.google.com/citations?user=Kfl6fVQAAAAJ
http://www.ucl.ac.uk/maths/courses/msc-financial
https://sites.google.com/site/algorithmictradingbook/

# Sebastian Jaimungal - high frequency trading
http://www.utstat.utoronto.ca/sjaimung/

# Brandt Fuqua high frequency data intertemporal portfolio choice
http://people.duke.edu/~mbrandt/index.html

# Paolo Guasoni - Alpha Actively Managed Funds Optimal Consumption Utility very mathematical
http://www.guasoni.com/
http://www.bu.edu/math/people/faculty/mathematical-finance/guasoni/
http://www.dcu.ie/info/staff_member.php?id_no=3877

# Blake LeBaron agent based models nonlinear time series BDS test sofware
http://people.brandeis.edu/~blebaron/soft.html
# Santa Fe Artificial Stock Market simulation
http://artstkmkt.sourceforge.net/
The stock market simulation has agents following different trading rules.
The simulation exhibits bubbles and crashes.
The model finds an inverse relationship between diversity and asset price.
As an asset price reaches its peak, the diversity of trading rules 
reaches its trough. (JP: I guess other trading rules are stop-lossed out?)

# Detecting Critical Transitions in Time series and Spatial data
http://www.early-warning-signals.org/home/
http://www.early-warning-signals.org/resources/code/
http://www.early-warning-signals.org/time-series-methods/metric-based-indicators/bds-test/
http://www.early-warning-signals.org/time-series-methods/metric-based-indicators/
http://www.early-warning-signals.org/time-series-methods/model-based-indicators/
# earlywarnings package
http://www.vasilisdakos.info/
http://www.early-warning-signals.org/resources/code/
https://github.com/earlywarningtoolbox
http://cran.r-project.org/web/packages/earlywarnings/index.html

# Wageningen University complex systems critical transitions
http://www.sparcs-center.org/
# Self-organized allocation to research crowdfunding democratization decentralization
http://www.sparcs-center.org/news/44/21/Self-organized-fund-allocation-SOFA.html
https://omslag.de/onderzoeksfinanciering/de-wijze-massa/

# William A. Brock economist Growth Theory Nonlinear Dynamics complex systems
https://en.wikipedia.org/wiki/William_A._Brock

# Riccardo Rebonato
http://www.marketswiki.com/mwiki/Riccardo_Rebonato
http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=2146897

# German Creamer - Stevens Institute of Technology
http://web.stevens.edu/research/faculty_profile.php?faculty_id=1448
http://www.creamer-co.com/

# Carl Bacon book Practical Portfolio Performance Measurement and Attribution

# Andreas Steiner - consultant dislikes alpha
http://www.andreassteiner.net/
https://www.linkedin.com/in/andreassteiner
http://www.andreassteiner.net/consulting/
http://www.andreassteiner.net/consulting/publications.html



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### traders strategists investors

# Blair Hull CEO of Ketchum Trading
http://www.investopedia.com/news/3-trading-lessons-blair-hull/
# HTUS Hull Tactical ETF
https://finance.yahoo.com/quote/HTUS/?p=HTUS

# Jens Nordvig top currency and macro strategist at Bridgewater Associates founder of Exante Data
http://jensnordvig.com/
http://exantedata.com/
http://www.bloomberg.com/news/features/2016-09-15/wall-street-s-0-01-the-guru-who-only-talks-to-hedge-fund-elite



###########
### thoughts

I'm more interested in building an assembly line of quantitative trading models, rather than crafting a single trading model.
Bring quantitative trading models into the industrial age. 

# Prerequisites for algorithmic trading strategies
Algorithmic trading requires a pipeline of strategies, not just a single strategy that looks promising. 
Algorithmic trading requires a strategy lifecycle. 
The critical question is when should a strategy be retired?  If it hits a stop-loss?  How to determine stop-loss amount?  Using bootstrap of backtesting, to determine potential drawdowns and distribution of potential returns. 

Strategies that trade infrequently can't really be tested properly. 
Strategies that identify a few successful trades 
High frequency data is needed for quant strategies for several reasons. 
machine learning isn't effective for strategies that trade infrequently (weekly, monthly)
machine learning requires trading events 
strategies which trade more often (intraday)
for learning

bias-variance tradeoff

Optimization in the presence of noise leads to spurious 



# The reason skewness caught my attention as an indicator is because of the Amaya paper that I cited in my presentation.  There's growing research showing that positive skewness in stock returns may predict their lower future returns.  The analogue is buying options.  When you buy an option you get positively skewed returns, but you must also pay an option premium, which lowers future returns.  

# Finance theory states that it's not possible to obtain positive returns without taking risks (there's no free lunch)
One of the basic principles of finance theory is the relationship between risk and return on investment.
It doesn't mean that taking risk guarantees positive returns, because there are many strategies with lots of risk but no positive returns.  
It means that the best performing strategies that do produce positive returns can't avoid taking risks.
It also means that strategies that don't take any risks can't produce positive returns.

# Capital Asset Pricing Model (CAPM) states that higher returns are only possible by taking more risk
We first select all the strategies that take the same level of risk, and then we select the best performing strategy from those strategies.  
Then we compare the returns of these best performing strategies with their levels of risk.
The CAPM states that the returns of the best performing strategies are proportional to their levels of risk.
In other words, you can't obtain higher returns without taking more risk.  
But the CAPM doesn't promise higher returns for taking more risk, since ther are many strategies with lots of risk but no positive returns.  

# Finance theory states that arbitrage isn't possible in well functioning markets
Arbitrage means buying something at a lower price and immediately selling it at a higher price.  
Arbitrage is a strategy that doesn't take any risks, and finance theory states that it therefore shouldn't be able to produce positive returns either.  
Even though arbitrage shouldn't be possible in well functioning markets at equilibrium, arbitrage is often possible in real markets.

# Statistical arbitrage involves buying something at a lower price and selling it later at a higher price, or vice versa, first selling it and later buying it back at a lower price.  
Statistical arbitrage is based on past statistical observations of price patterns, and occurs on a scale of several days or less.  
Statistical arbitrage isn't riskless, since it involves owning a position for some time.  
But statistical arbitrage is able to reduce risk on average over many trading cycles.  

# Investors have a desire for assets with positive skewness, similar to lottery tickets (options). 
This may be caused by 
reduces their future returns. 

low beta anomaly caused by demand for positive skewness (lottery) which reduces future returns  
Bali Betting Against Beta Lottery Demand.pdf  
Recent academic research is suggesting the existence of risk premia associated with higher moments of the return distribution.
For example skewness (third moment) is believed to predict lower future returns.
This may be caused by investor demand for assets with positive skewness (lottery tickets), which reduces their future returns. 
We explore this phenomenon using high frequency data, since it allows for more accurate and timely estimation of higher order moments (bias-variance tradeoff).
Furthermore, we also explore the interaction between skewness and the momentum factor, since some recent research is suggesting that momentum returns are enhanced by skewness.
But using high frequency data can present a challenge, since the data size may cause R programs to run very slowly.
We demonstrate how to perform the analysis using vectorized R code designed for speed.
We present our results using interactive visualizations afforded by new R packages.



###########
### trading strategy ideas:

# is VIX trading volume able to forecast returns, or is volume just a proxy for volatility?

# VIX Futures term structure strategies
http://quantpedia.com/Screener/Details/198

# VIX/SPX trading strategies
http://www.bloomberglabs.com/quantitative-finance/research/vixspx-trading-strategies/
SPX mean reversion strategy using the spread between daily and weekly realized variance
VIX futures pair/spread strategy using the term-structure of VIX futures

# short SPX ATM puts strategy
http://www.thestreet.com/story/12770794/1/a-simple-options-trading-strategy-that-beats-the-sp-500.html

# test strategy where you exploit trending into market close:
jump on the trend in the last hour, then reverse position at the close, and reverse again at next day open

# perform portfolio optimization using two time scales (lookback periods)
estimate correlation matrix on a long lookback period
estimate returns on a short lookback period
the bias-variance tradeoff for correlation should be different than for returns
it's better to estimate returns on a short lookback period, but there's 
no benefit to use short lookback period for estimating correlation 

# perform running portfolio optimization assuming zero or constant correlations
update correlations over longer lookback periods 
perform running portfolio optimizations over shorter lookback periods 
invest in the portfolio based on its trailing return
use variance forecasts (instead of realized variance) to estimate portfolio variance 
demonstrate that this portfolio is forecastable in-sample
demonstrate that it's also forecastable out-of-sample
explore shrinkage: does it improve forecastability?
explore long-only weights: that is just index (market portfolio)
explore mean reverting strategy: over intra-day period apply rule:
if realized return is higher than expected, then reduce long position, and vice versa
similar for expected negative return

# instead of optimizing portfolio for highest Sharpe, optimize for highest forecastability
is highest forecastability similar to lowest variance?
answer: highest Hurst exponent 
can Hurst exponent be forecast?
create scatterplot of many portfolios: variance versus Hurst exponent 
use package ForeCA by Georg M. Goerg at Google NYC
http://fastml.com/are-stocks-predictable/
http://stats.stackexchange.com/questions/126829/how-to-determine-forecastability-of-time-series
http://stats.stackexchange.com/questions/23007/assessing-forecastability-of-time-series?lq=1

calculate portfolio indicator similar to Sharpe: portfolio return divided by volatility

# forecastable component analysis package ForeCA
# find beta that maximizes Hurst
portfolio of stock plus beta times index
what is the beta that maximizes the portfolio's Hurst exponent?
is the beta stable over different intervals out-of-sample?
http://artax.karlin.mff.cuni.cz/r-help/library/ForeCA/html/ForeCA-package.html

# estimating Hurst exponent C++ code
http://www.bearcave.com/misl/misl_tech/wavelets/hurst/
http://www.bearcave.com/misl/misl_tech/wavelets/hurst/doc/index.html

# regress running Hurst exponent versus running volatility
regress running Hurst exponent versus returns over same lag

# if variance can be forecast, then forecasts can be used in pairs rebalancing trading
buy SPX and VIX and rebalance when SPX becomes cheap and vice versa
entry points are determined by forecasted volatility of SPX and VIX

# study periods of high volatility (variance) spikes
calcuate histogram of volatility - what's the distribution? chi-squared?
fit the chi-squared distribution using MoMoments

# identify periods around volatility spikes - say 100 bars before and after
what's the distribution of returns from period start to peak volatility,
and from peak volatility to period end?

# calculate Hurst exponent for volatility spikes, before min and after min
are returns autocorrelated around volatility spikes? yes
draw scatterplot of daily Hurst exponent versus volatility
draw scatterplot of Hurst exponent versus volatility spikes

create ARIMA model where autocorrelation term is weighted by level of VIX
create ARIMA model with correlation of returns to level of VIX
	negative correlation means returns are skewed to downside when VIX is high

# calculate daily seasonality of Hurst exponent

# forecast stock index using VIX
high VIX produces high future returns
invest SPX notional inverse to VIX level
hysteresis: buy SPX at higher VIX, sell SPX at lower VIX, 
Martin VIX Variance Stock Premium Forecasting.pdf
Bollerslev Stock Forecasting Volatility Market Premiums.pdf
Campbell Stock Forecasting.pdf

# create basic futures strategies:
# VIX/SPY
# VIX/VIX term
# VIX/TSY
# TSY/TSY term
Nagel Liquidity Premium Mean Reversion VIX Strategy.pdf
Donninger VIX Futures Basis Strategy.pdf

# VIX Futures ETF UVXY  
http://www.bloomberg.com/news/articles/2016-04-22/record-vix-bets-keep-surging-as-wall-street-divines-mixed-signal

# study trading strategy that is short at period start, then flips long 
and rides recovery

# forecast price bottom or volatility peak from moving averages
is there some scaling law?

# forecast variance using different models
variance can be forecast using GARCH
create indicator of variance trend from two VW Avg Variance
does the forecast variance also help to forecast returns?

# divide time into periods of low volatility and high volatility
calculate Hurst exponent in each period

# create trading strategy 
thesis: volatility is forecastable, 
returns are forecastable in periods of high volatility
identify when volatility will spike, and follow return trend in periods of high volatility

# perform Gram–Schmidt procedure to obtain PCA
repeat Gram–Schmidt procedure but maximize Hurst or SR, instead of variance
interpret the max Hurst components and observe how they change
regularize the procedure (shrinkage)
regress the max Hurst components against SPY

# draw scatterplot of daily SD versus daily returns

# create trading strategy with dual indicators
slow indicator for momentum and fast indicator for mean reversion
trade only on fast indicator, but bias its positions using slow indicator
when slow indicator indicates long position, 
then fast indicator should switch between long and flat positions, and vice versa

# volatility reduction translates into growth
Kelly criterion

# volatility estimation
Stroud High Frequency Forecasting Volatility VIX VXX Strategy

# volatility pumping strategy
http://www-isl.stanford.edu/~cover/portfolio-theory.html

# Thomas Cover Universal Portfolios
https://en.wikipedia.org/wiki/Universal_portfolio_algorithm
Cover Universal Portfolios MathFin.pdf
Cover Universal Portfolios.pdf

Witte Trading Volatility Pumping Harvesting.pdf
Witte SaP500_HistPrices.R
Witte FTSE_DAX_DJIA_FollowWinners.R
Heaton Stock Selection Active Portfolio Management.pdf
Johannes Stock Forecasting Intertemporal Universal Portfolios Choice.pdf

http://optimallog.blogspot.com/2012/06/universal-portfolio-part-6.html
http://optimallog.blogspot.com/2012/07/universal-portfolio-part-9.html
http://optimallog.blogspot.com/2012/08/universal-portfolio-part-10.html

# Parrondo's paradox Brownian ratchet
https://en.wikipedia.org/wiki/Parrondo%27s_paradox
http://www.cut-the-knot.org/ctk/Parrondo.shtml
http://seneca.fis.ucm.es/parr/GAMES/Paradox%20in%20Game%20Theory%20Losing%20Strategy%20That%20Wins.htm
http://www.nytimes.com/2000/01/25/science/paradox-in-game-theory-losing-strategy-that-wins.html

http://parrondoparadox.blogspot.com.es/2011/02/parrondos-paradox-stock-market.html
http://quant.stackexchange.com/questions/1710/proof-that-you-cannot-beat-a-random-walk
http://quant.stackexchange.com/questions/352/volatility-pumping-in-practice
http://quant.stackexchange.com/questions/10887/harnessing-small-correlations-for-reliable-profit

# Brownian flashing ratchet
https://en.wikipedia.org/wiki/Brownian_ratchet
http://www.imsc.res.in/~sitabhra/research/persistence/page1.html
http://www.imsc.res.in/~sitabhra/research/persistence/page2.html
http://www.imsc.res.in/~sitabhra/research/persistence/page3.html

# risk parity strategy
First day of every month, sell all portfolio holdings at market. Same day, buy 50 random stocks from the S&P 500 constituents. Weight positions by inverse volatility, i.e. simple vola parity sizing. Always be fully invested.
http://www.followingthetrend.com/2015/06/a-random-ass-kicking-of-wall-street/

# risk parity by Bridgewater Associates
https://www.linkedin.com/pulse/our-thoughts-risk-parity-all-weather-ray-dalio?trk=hp-feed-article-title-ppl-follow


# use turning points in bearish percentage as buy/sell signals
http://www.zerohedge.com/news/2015-02-16/secular-death-market-bears-continues


# Two-Envelope Paradox
http://phys.org/news/2009-08-strategy-two-envelope-paradox.html


# send email to Josh at OneTick

# Random forest simple illustration
http://www.analyticsvidhya.com/blog/2014/06/introduction-random-forest-simplified/
http://www.analyticsvidhya.com/blog/2014/06/comparing-cart-random-forest-1/

# hosting Python and R models
https://yhathq.com/
https://github.com/yhat

# Raffael Vogler
http://www.joyofdata.de/blog/testing-linear-separability-linear-programming-r-glpk/
http://www.joyofdata.de/blog/category/machine-learning/
http://www.joyofdata.de/blog/

# Decision trees in Tableau using R
http://boraberan.wordpress.com/2014/02/07/decision-trees-in-tableau-using-r/

https://medium.com/code-poet/machine-learning-is-fun-80ea3ec3c471

# Replicate Paper with R and rCharts: AQR Dispelling Myths of Momentum 
http://timelyportfolio.github.io/rCharts_factor_analytics/aqr_fact_fiction_momentum.html
https://github.com/timelyportfolio/rCharts_factor_analytics/
http://timelyportfolio.blogspot.com/2014/06/dispelling-myths-of-momentum-aqr.html
http://seekingalpha.com/article/2226863-myths-about-momentum-part-i
http://seekingalpha.com/article/2229023-myths-about-momentum-part-ii

http://quantstrattrader.wordpress.com/

# take Tour of Machine Learning Algorithms
http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
# Build a Machine Learning Portfolio: Curate Your Models Like a Gallery
http://machinelearningmastery.com/build-a-machine-learning-portfolio/
http://machinelearningmastery.com/machine-learning-that-matters/

# explore publishing on:
https://medium.com/




###########
### Papers to read

Taleb Silent Risk.pdf

Cartea Algorithmic Trading with Learning.pdf
https://sites.google.com/site/algorithmictradingbook/

Qian Multiple Classifiers Stock Forecasting

German Creamer Logitboost Trading

Carr Optimal Trading Rules Without Backtesting

Rinne Seasonality Turn of the Month Stock Returns

DeMiguel Optimal Simple Portfolio Diversification

Prado Moment Skewness Kurtosis Gaussian Mixture Performance Tracking

Bailey Prado Strategy Overfitting Cross-validation
Bailey Prado Portfolio Optimization Algorithm

Faber Relative Strength Investment Strategies

Pav Markowitz Portfolio Distribution

Pav Markowitz Portfolio Signal Noise Ratio

Gatheral Random Matrix Theory Covariance Estimation

Bailey Prado Deflated Sharpe Ratio Overfitting

Daniel Momentum Crashes

Amaya Skewness Momentum Equity Returns
"We find a very strong negative relationship between realized skewness and next week's stock returns."
"A trading strategy that buys stocks in the lowest realized skewness decile and sells stocks in the highest realized skewness decile generates an average weekly return of 24 basis points with a t-statistic of 3:65."
Yang Volatility Filtering Technical Trading
Han Sorted Portfolios Trading Anomaly
Blitz Short-Term Residual Reversal
QUSMA ETF Daily Mean Reversion
Rudy Mean Reversion After Large Drops

Prado Backtesting Overfitting
Bailey Backtesting Overfitting
Bailey Backtesting Overfitting Cross-validation
Lo Adaptive Markets Hypothesis
Sutton Ensemble Learning Bagging Bootstrapping Boosting AdaBoost
Sutton Classification Evaluation Cross Validation Hypothesis Testing

Moskowitz Time Series Momentum
Bandarchuk Volatility and Momentum
Frazzini Buffett’s Alpha
Asness Quality Minus Junk
Blitz Global Tactical Cross-Asset Allocation
Israel Size Value Momentum Anomalies
Keller Shrinkage Momentum Asset Allocation
Keller Momentum Markowitz Asset Allocation
Mitra Hurst Exponent Forecasting Financial Time Series
Grossmann Inverse Volatility Trading

Charpentier Bayesian Analysis Frequentist Hypothesis Testing.pdf
Masson Bayesian Analysis Frequentist Hypothesis Testing.pdf



###########
### suggest stackoverflow to dummies

You may want to submit your question to Stack Overflow:
http://stackoverflow.com/

In my opinion Stack Overflow is the best question and answer forum for programming.
But be sure to follow the rules of posting questions.



###########
### jobs in past


###########
### NYU Tandon School of Engineering

http://engineering.nyu.edu/academics/departments/finance/people

http://www.nyu.edu/search.directory.html?search=name%3Dpawlowski%26location%3DNew+York

http://engineering.nyu.edu/life/student-resources/registrar/academic-calendar#fall14

# Roy S. Freedman Inductive Solutions Artificial Intelligence Compliance Credit Interest Rate Models
http://en.wikipedia.org/wiki/Roy_S._Freedman
http://www.inductive.com/index.htm
http://www.inductive.com/rsfbio.htm



###########
### people I know

# Jonathan Stein
http://www.energyriskusa.com/static/jonathan-stein


###########
### R/Finance Chicago

### R/Finance Chicago 2016 best presentations
Jason Foster
Douglas Service
Qiang Kou
Piotr Orlowski
Bryan Lewis
Mark Bennett
Matt Brigida
Michael Kane
Xiao Qiao
Frank Diebold
Eran Raviv



###########
### news articles

# news blog data c++
https://tfetimes.com/

# (09-12-16) Volatility targeting and risk parity quant funds may be forced to sell in a market selloff as volatility andd correlations increase
http://www.bloomberg.com/news/articles/2016-09-12/a-sell-off-pressure-larger-than-brexit-imperils-one-of-wall-street-s-hottest-trades

# (09-09-16) Bridgewater Pure Alpha fund has declined -9.4% in 2016, while All Weather gained +13.5%
http://www.bloomberg.com/news/articles/2016-09-11/bridgewater-attracts-22-5-billion-financial-times-says
The Pure Alpha fund has returned about 12% per year since 1991, and hasn’t had a losing year in the last 15.
The Pure Alpha fund owns $69 billion of assets. 
Despite the loss, the Pure Alpha fund is receiving $22.5 billion from investors, after it opened to new money.

# (09-09-16) Investors in 2016 have redeemed the most capital from traditional hedge funds since 2008
http://www.bloomberg.com/news/articles/2016-09-09/perry-capital-assets-plunge-60-to-4-billion-as-wagers-backfire

# (09-07-16) Banks and investment funds are hiring quants
http://www.bloomberg.com/news/articles/2016-09-09/ubs-wealth-management-s-haefele-joins-battle-to-recruit-quants

# (09-01-16) Jaffray Woodriff and David Vogel are using machine learning and their Voloridge and QIM funds are strongly outperforming
http://www.bloomberg.com/news/articles/2016-09-01/code-bros-united-by-netflix-run-two-of-world-s-best-quant-funds

# (08-21-16) Simplex Equity Futures aglorithmic fund in Tokyo is outperforming other hedge funds
http://www.bloomberg.com/news/articles/2016-08-21/hedge-fund-robot-outsmarts-human-master-as-ai-passes-brexit-test
The Simplex fund is up 1.9% from its inception in April 2016 through August 19th, while the Topix index has lost 16% in 2016.

# (08-17-16) Quantitative funds are attracting capital as they continue to outperform other hedge funds in 2016
http://www.bloomberg.com/news/articles/2016-08-21/hedge-fund-robot-outsmarts-human-master-as-ai-passes-brexit-test
Two Sigma was founded by David Siegel and John Overdeck, and now manages $35 billion.
David Siegel worked for Tudor Investment, and Two Sigma was seeded by Paul Tudor Jones in 2001. 
Steven Cohen invested $250 million in Quantopian.

# (07-15-16) S&P500 has reached a new high but defensive stocks are leading the rally, and stock picking funds have not profited despite low stock correlation
http://www.ft.com/cms/s/0/ede9f018-49cf-11e6-b387-64ab0a67014c.html
Both stocks and bonds have rallied in anticipation of further low interest rates and central bank stimulus.
Low volatility stocks and high-yielding stocks have outperformed, because they are substitutes for bonds. 
Defensive stocks such as utilities, real estate and telecoms have also outperformed, which is typical for the end of the business cycle, before profits fall and a recession starts.
Stock correlation has dropped since May 2015, with 109 stocks gaining at least 20%, and 96 falling at least 20%. 
But stock picking funds have not profited, with equity hedge funds producing a small negative return in 2015, and a small negative return so far in 2016. 

# (07-01-16) All assets (stocks, bonds, and commodities) rallied after Brexit, because markets are expecting central banks to be dovish
http://www.bloomberg.com/news/articles/2016-07-01/everyone-s-a-winner-in-brexit-aftermath-as-doves-rescue-market

# (06-30-16) Soros says Europe is in a deflationary trap that will get worse because of Brexit
http://www.bloomberg.com/news/articles/2016-06-30/soros-says-brexit-has-unleashed-a-financial-markets-crisis
The EU will disintegrate unless voters see that the EU is benefiting their lives.

# (06-29-16) British stocks have erased all Brexit losses, and have outperformed eurozone stocks
http://www.zerohedge.com/news/2016-06-29/brexiteers-1-0-scaremongers-uk-stocks-erase-all-brexit-losses
http://www.bloomberg.com/news/articles/2016-06-29/it-took-just-four-days-for-british-megacaps-to-shake-off-brexit
If British stocks continue outperforming, then it will create a big incentive for other EU members to exit as well.

# (06-24-16) Soros didn't short British pound before Brexit vote, but he shorted stocks and bought gold, which both made him money
http://www.marketwatch.com/story/soros-looks-set-to-make-a-killing-on-brexit-result-2016-06-24
http://www.bloomberg.com/news/articles/2016-06-27/soros-was-long-the-pound-before-brexit-vote-says-spokesman

# (06-24-16) gold rallied up to 8% after Brexit vote
http://www.bloomberg.com/news/articles/2016-06-26/hedge-funds-win-world-beating-rally-with-record-gold-holdings

# Brexit is extremely negative for European markets
http://www.zerohedge.com/news/2016-06-24/who-are-biggest-losers-brexit
The British stock market was the best performing European stock market on Friday after the Brexit vote.

# (06-15-16) Quantitative funds are outperforming other hedge funds in 1H16, up about 8% versus other hedge funds flat
http://www.bloomberg.com/news/articles/2016-06-15/machines-beat-humans-in-hedge-fund-quest-to-time-market-bottom
The biggest shift in momentum was in energy stocks. 
Quantitative funds identified long energy stock trades both based on valuation and on momentum.

# (05-30-16) Quantitative equity funds have had strong returns, and now use big data techniques
http://www.pionline.com/article/20160530/PRINT/305309990/quants-throw-tradition-out-the-window
Acadian Asset Management $9.2 billion global managed volatility equity strategy beat its MSCI World index benchmark by 12.63% for the 12 months through March 31, while Analytic's $3.8 billion global low-volatility equity strategy exceeded its benchmark by 9.01% and Martingale's U.S. low-volatility large-cap equity strategy added 8.2% for the same period.

# (05-31-16) Value stocks have underperformed growth stocks since 2006, the longest losing stretch in history
http://www.bloomberg.com/news/articles/2016-05-31/lost-decade-for-value-stocks-tests-faithful-who-say-end-is-nigh
In the 1990s growth stocks outperformed drastically, then in 2000-2006 value stocks outperformed drastically.
Currently banks make up about 30% of large-cap value stocks, and their underperformance since 2006 largely explains the underperformance of value stocks.
Low interest rates have held back bank stocks, and the repeated failure of rate hikes have kept bank stock prices low. 
But 2016 may be a turning point for value stocks.
Value stocks in the Russell 1000 Index have gained 4.3% in 2016, compared with 1.1% for growth and 2.7% in the S&P500. 
The KBW Bank Index has jumped 18% in the past three months, double the increase of the S&P500. 
Strong global economic growth is needed for value stocks to outperform. 

# (05-25-16) Quant fund XTX Markets is fourth biggest foreign exchange trader, with 7.6% of spot volume, ahead of Deutsche Bank
http://www.bloomberg.com/news/articles/2016-05-25/computerized-trader-leapfrogs-deutsche-bank-in-currency-rankings
XTX also makes markets in commodities, stocks and derivatives. 

# (05-15-16) Quant hedge funds have been outperforming macro, merger arbitrage, and other funds in 2015 and 2016
https://www.theguardian.com/business/us-money-blog/2016/may/15/hedge-fund-managers-algorithms-robots-investment-tips
Stock picking by human experts has become very risky. 
David Einhorn recommended buying SunEdison before it filed for bankruptcy. 
Bill Ackman promote Valeant before its stock crashed. 
David Siegel, cofounder of Two Sigma Investments, stated that one day no human investment manager will be able to beat the computer algorithm. 

# (05-12-16) Clifford Asness: hedge fund returns are very correlated to the market, and therefore don't jsutify their high fees
http://www.bloomberg.com/view/articles/2016-05-12/hedging-on-the-case-against-hedge-funds
Hedge funds have a beta of about 35% to 40%. 
Hedge funds need to hedge more of their beta, and charge lower fees. 

# Hedge funds lost 1.9% in 1Q2016, the worst start since 2008
http://www.bloomberg.com/news/articles/2016-05-11/rubenstein-says-he-s-surprised-macro-hedge-funds-got-it-wrong
979 funds closed in 2015, more than any year since 2009. 

# Manoj Narang creates new firm to combine high frequency trading with statistical arbitrage strategies
http://www.bloomberg.com/news/articles/2016-03-11/a-high-speed-trading-pioneer-spots-a-new-way-to-make-big-returns
High frequency trading relies on "structural" information about the order book, and profits mostly on front-running other traders by guessing their intentions. 
High frequency trading uses prices and order book data at around milisecond frequencies, and holds positions for seconds or less, and requires very fast connections to exchanges. 
Statistical arbitrage identifies statistical anomalies in asset prices, and profits by betting that those anomalies will revert back to equilibrium values. 
Traditional statistical arbitrage uses daily prices to identify price anomalies, and holds positions for at least several days.  
Manoj Narang is a veteran high frequency trader who recently left Tradeworx. 
Manoj Narang's new fund will focus on volatility arbitrage. 

# (04-05-16) Stock picking funds in 1Q16 have underperformed the market by the most in two decades
http://www.bloomberg.com/news/articles/2016-04-05/lockstep-moves-ease-futility-doesn-t-for-u-s-equity-managers
Correlation between stocks is at the lowest levels since 2012, which should be good for stock pickers. 
But stock picking funds owned the wrong stocks, because the stocks that were unloved by stock picking funds outperformed the ones they loved by the most since 2013, with lower quality stocks rallying.
Portfolio managers made a mistake by avoiding utility and consumer staples stocks, while buying financial and technology stocks.
Stocks that performed the worst in 2015 rallied by 8.2% in the first quarter of 2016.
Momentum strategies also lost money because trends quickly reversed, as the S&P first lost 10% but then recovered. 

# (04-06-16) Tiger Global hedge fund plunged -22% in the first quarter 2016.
http://www.wsj.com/articles/tiger-global-hedge-fund-plunges-on-amazon-netflix-other-bets-1459961411

# (03-28-14) Kyle Bass fed negative rumors to CNBC while selling short mortgages
http://www.wsj.com/articles/goldman-sachs-and-bear-stearns-a-financial-crisis-mystery-is-solved-1459205026
On Wednesday, March 12, 2008, CNBC reporter David Faber asked Bear Stearns chief Alan Schwartz if it was true that Goldman Sachs wouldn’t accept Bear Stearns counterparty risk.
David Faber got the story from Kyle Bass. 
This rumor led to the collapse of Bear Stearns. 

# (03-03-14) Asness: Great Divide over Market Efficiency
http://www.institutionalinvestor.com/Article/3315202/Asset-Management-Equities/The-Great-Divide-over-Market-Efficiency.html

# (03-24-16) Most active bond traders are losing money in 2015-16, despite bonds rallying
http://www.bloomberg.com/gadfly/articles/2016-03-24/if-every-bond-s-a-winner-why-are-there-so-many-losers
The Merrill Lynch Global Bond Index has gained almost 3% in 2016, the biggest gain in the same period since 1997. 
But the returns at nontraditional bond funds are slightly negative on average in 2016, and some have lost a lot. 
The Altegris Fixed Income fund is down more than 8% and the Highland Credit Fund has lost more than 6% year to date 2016. 
European hedge funds have struggled, with Oceanwood Capital $2.1 billion hedge fund losing 8.1% through March 12, 2016.  
The winners are central banks who own the sovereign bonds that have experienced the biggest gains.

# Small quant funds grow by using open-source software and cheap data
http://www.bloomberg.com/news/articles/2016-03-16/barbarian-coders-at-the-gate-the-inexorable-rise-of-diy-quants
Huge leaps in computing power are transforming modern finance in ways that few have ever imagined and giving tech-savvy, DIY types the tools to compete with large quantitative funds. 
Emanuel Derman says there's so much available data, open-source software, and computing power, that samll quant funds can get started in no time at all.
Open-source software written in R and Python is lowering the barriers to entry for small quant funds. 
But growth of quant funds is leading to crowded trades which may have caused the August 24th 2015 flash crash. 
Ernie Chan and Roger Hunter run QTS Capital Management. 
QTS buys futures tick data from AlgoSeek for $500 a month.  
QTS returned 12% in 2015, outperforming the U.S. stock market and the average hedge funds. 
Since 2014 QTS assets have grown more than fivefold to $22 million.
On any given day QTS is testing 10 different models while executing eight trading strategies.
Quantopian started in 2011 and its membership has increased from 1,570 to more than 66,000 in 2016.
Estimize provides crowd-source corporate earnings estimates.

# Peter Muller hedge fund PDT
http://www.forbes.com/sites/nathanvardi/2016/01/04/the-new-quant-hedge-fund-master/

# David Siegel and John Overdeck hedge fund Two Sigma
http://www.forbes.com/sites/nathanvardi/2015/09/29/rich-formula-math-and-computer-wizards-now-billionaires-thanks-to-quant-trading-secrets/
John Overdeck worked for D.E. Shaw and Amazon.
Two Sigma’s two main funds are Eclipse and Spectrum.  Eclipse has a shorter holding period of several weeks, while Spectrum has a holding period closer to one month.
Two Sigma is well known to pursue in court its employees for stealing proprietary models or data.
Two Sigma accused researcher Jianjun Qiu of stealing their proprietary models or data.  Qiu escaped to China, reached a settlement with Two Sigma, and now works for Citadel.

# The flash rally in U.S. Treasuries on October 15, 2014 was caused by illiquidity in cash market
http://www.bloomberg.com/news/articles/2016-02-26/jpmorgan-s-flash-rally-theory-contains-message-on-today-s-market

# (02-28-16) Early 2016 selloff led by most popular analyst stock picks
http://www.bloomberg.com/news/articles/2016-02-28/analyst-darlings-turn-stock-market-pariahs-as-momentum-unravels
Stocks ranked highest by analysts have fallen on average 11% in 2016, while stocks ranked lowest are down only 3.4%.
This is tied to the reversal of high momentum stocks, many of which are owned by hedge funds.  
The stocks that are the most owned by hedge funds are trailing the S&P500 by 4.6% in 2016, after outperforming by an average of 10% per year over the last four years.
In March 2009 analysts were bearish when it was the ideal time to buy stocks.

# (02-23-16) Hedge funds underperformance in early 2016 caused by large exposure to momentum factor, which has reversed
http://www.zerohedge.com/news/2016-02-23/how-outperform-most-hedge-funds-2016
Hedge funds have large positions in high momentum stocks, they have large exposure to the momentum factor.
Goldman Sachs created a Hedge Fund VIP basket of stocks to which many hedge funds large exposures (Bloomberg ticker: GSTHHVIP).
Many hedge funds own similar stocks, and most of those belong to the VIP basket. 
Many hedge funds are also very concentrated in a small number of stocks, with the average fund holding 68% of long assets in its top 10 positions. 

# Trading firms and hedge funds are hiring quants who combine skills in trading, modeling and programming
http://www.nytimes.com/2016/02/23/business/dealbook/a-new-breed-of-trader-on-wall-street-coders-with-a-phd.html
https://www.janestreet.com/
Quant firms have prospered and grown, creating demand for traders who can build financial models and write computer code, and who can also spot market anomalies and bet big on them.
Issuing new ETFs has become a major profit center for large asset management firms like BlackRock, Vanguard and Invesco.
Jane Street specializes in trading ETFs, and has increased its shareholder equity from $228 million in 2007 to more than $1 billion in 2015.
Jane Street employs only 450 people in offices in New York, London and Hong Kong.
Yaron Minsky is the CTO of Jane Street.
Sandor Lehoczky is a star trader at Jane Street and chief quant. 

# (02-22-16) Arnott Research Affiliates: value stocks are cheapest ever, besides the Nifty Fifty craze of 1970s
http://www.bloomberg.com/gadfly/articles/2016-02-22/don-t-be-tricked-by-the-alpha-mirage-from-smart-beta

# (01-27-16) Hedge fund underperforming massively, but investors still want them
http://www.bloombergview.com/articles/2016-01-27/investors-search-for-new-love-in-hedge-funds
http://www.bloomberg.com/news/articles/2016-01-26/bridgewater-s-dalio-trumps-soros-as-most-profitable-hedge-fund
The 20 most profitable hedge funds earned $15 billion in 2015, while the rest collectively lost $99 billion. 
The top funds have earned 48% of the $835 billion in profits that the hedge fund industry has generated since its inception.

# (01-20-16) Best and worst stocks are diverging the most since the 1990s
http://www.bloomberg.com/news/articles/2016-01-21/short-sellers-are-making-an-even-bigger-killing-than-you-think
The top 10% most costly to short underperformed the market by a record 26% in 2015, which is the widest split between winners and losers since the 1990s.  Even as the S&P dropped only 0.7% in 2015, the average decline in the 10 worst stocks was 64%, while the 10 best surged 71%.  This divergence is mostly due to commodity companies underperforming and some technology companies outperforming. 

# Many quant indexes have underperformed compared to their backtests
Beware of optimistic results of backtesting hypothetical investment strategies!  Past backtesting results are no guarantee of future performance, as they say.
http://www.bloomberg.com/news/articles/2016-01-21/how-wall-street-finds-new-ways-to-sell-old-opaque-products-to-retail-investors

# Market depth is dropping despite high trade volumes
http://www.bloomberg.com/gadfly/articles/2016-01-15/robots-are-eating-your-retirement-in-volatile-stock-market
Market depth is how many contracts can be traded before moving the market by 1 point. 
Market depth has declined by more than 60% over the past 2 years, as was illustrated during the August 24th 2015 crash.
Most trading is done by algos, even the trades from actively managed funds.  

# (01-14-16) AQR ETFs are performing very well
http://www.bloomberg.com/news/articles/2016-01-14/cliff-asness-s-alternative-funds-top-rivals-with-17-returns
QMNIX AQR Equity Market Neutral Fund
QLEIX AQR Long-Short Equity Fund

# artificial intelligence machine learning startups in finance
http://themarketmogul.com/ai-and-finance-no-room-for-philosophy/
Kensho raised USD 15 million in order to train computers to replace financial analysts. 

# Marko Kolanovic head JPM Quants making well timed market calls
http://www.zerohedge.com/news/2015-11-13/jpmorgans-gandalf-quant-nailed-it-again
http://www.bloomberg.com/news/articles/2015-09-25/it-s-marko-kolanovic-s-market-we-just-trade-in-it

# Most active managers underperform because they risk missing a few outperforming stocks
http://www.bloombergview.com/articles/2015-11-11/why-indexing-beats-stock-picking
Stock index returns are driven by a small group of the best performing stocks 
in the index, while most of the rest perform poorly. 
Active managers risk missing those outperforming stocks. 
Active manager underperformance can be explained by creating random portfolios. 
Most random portfolios underperform the index because they're missing the 
outperforming stocks.  

# (08-18-15) momentum strategies losing steam in Summer 2015
http://www.bloomberg.com/news/articles/2015-08-18/momentum-trade-gives-evidence-of-its-own-mortality-a-down-week
Since July 2015 industry leadership in the S&P 500 has been shifting to utilities, which are leading the market since August, and 2014 winners, like health-care and consumer companies, are trailing. 
The Newedge CTA Index has fallen 6.5% from a high in April 2015.
Companies with strong balance sheets are rallying.
An index of stocks with the strongest balance sheets reached its highest level versus stocks with weaker ones since October 2013.

# (07-20-15) momentum strategies gained 15% in 2015
http://www.bloomberg.com/news/articles/2015-07-20/momentum-rules-in-u-s-stock-market-stuck-in-worst-rut-on-record
Momentum strategy of buying stocks with best price appreciation in recent quarters, 
and shorting the worst have gained 15% in 2015.
The top three industries in the S&P500 in 2015 are retailers, health-care, 
and biotech, which also led the market in 4Q2014, rising an average 14%.
While the S&P500 has not been up or down more than 3.5%, the difference in 
return between the best and worst-performing industries is widening, 
reaching the highest level since the bull market began in 2009.
Momentum strategies have beat every other quantitative strategies, 
including growth, value, and size, which has only happened two other times in 
24 years of data, in 2007 and 1993.
Momentum strategies work well in a maturing bull market such as now. 

# algorithmic trading for retail investors is becoming popular
http://www.wsj.com/articles/an-algo-and-a-dream-for-day-traders-1439160100

# CFM crash predictor equals volatility divided by square root of trading volume
http://www.bloombergview.com/articles/2015-04-06/bitcoin-and-market-crashes

# Capital Fund Management (CFM) Stratus fund returned 17.7% through October 2014, second-best large hedge fund in Europe
http://www.bloomberg.com/news/2015-01-06/the-quants-on-the-left-bank-how-paris-based-physicists-used-algorithms-to-get-big-returns.html
The return of volatility in 2014 has been good for quants,
Seven quantitative funds are in the top 25 funds in 2014,
Man Group stock rose 74.6% in 2014,
# Jean-Philippe Bouchaud - Capital Fund Management
https://www.cfm.fr/

# hedge funds ranking in 2014: Many quant funds showed good performance in 2014: AHL, Two-Sigma, Citadel, Graham, D.E. Shaw, etc.
http://www.bloomberg.com/news/2015-01-04/credit-hedge-funds-game-stupid-wall-street-to-top-2014-ranking.html

# more hedge fund losses
# They couldn't make money because volatility was too low.
# Now they can't make money because volatility is too high.
# "Volatility is too damn high!"
http://www.bloomberg.com/news/2014-11-09/macro-funds-that-lamented-boring-market-lose-in-october.html

http://www.businessweek.com/articles/2013-06-06/how-the-robots-lost-high-frequency-tradings-rise-and-fall

http://quant.stackexchange.com/questions/1628/what-are-the-best-sources-for-equity-quantitative-research
http://quant.stackexchange.com/questions/1985/what-papers-have-progressed-the-field-of-quantitative-finance-in-recent-years-p

# there may be ‘bursts of dependence’ leading to large cumulative losses
http://www.hussmanfunds.com/wmc/wmc141020.htm



###########
### behavioral finance

# Bloomberg Tradebook Trader Exercises
https://traderbrainexercise.com/
Denise Shull, founder of ReThink Group, New York consulting firm that coaches financial professionals and athletes:
Superior judgment of risk requires multiple cognitive and emotional abilities, that allow acquiring knowledge, recognition of reality, and understanding.  
These include the ability to create mental models to predict prices, emotional self-awareness (knowing when emotions interfere with good judgment), and the ability to read other people's minds.

# Andrew Lo experiments show that good traders have more control over their emotions, and are flexible in response to risk and market volatility
http://www.bloomberg.com/news/articles/2016-09-01/wall-street-s-next-frontier-is-hacking-into-emotions-of-traders
Andrew Lo observed volunteers in a simulated trading game, and measured the trader's stress levels by measuring their perspiration and heart rate.
Lo's study found that good traders responded to stressful situations quickly, but afterwards calmed down quickly as well, leaving them better prepared for future challenges. 
On the other hand, poor traders remained emotionally charged even after the volatility subsided. 
Lo’s study suggests that there’s a sweet spot for emotional engagement: too much, and you’re overly aggressive or fearful; too little, and you aren’t involved enough to care. 
Having too much or too little emotion made for poor trades. 


# Caltech researcher Peter Bossaerts found that traders use "theory of mind" when they make trading decisions
http://www.bloomberg.com/news/articles/2016-08-19/neuroscience-may-help-train-your-brain-for-trading
fMRI scans show that parts of the brain associated with the "theory of mind" are activated when traders try to forecast prices.
Traders that were better at predicting prices were also better on other tests of "theory of mind" abilities.

# People who are unsure and slow to make judgments are better decision makers
https://hbr.org/2016/07/slow-deciders-make-better-strategists
Researchers created a competitive strategy game, and analyzed the performance of different types of participants, based on their speed and confidence. 
The game participants were categorized into four groups according to their speed of making judgments, and their own confidence of their judgments.
Older men were over-represented among the two groups with high confidence, while women were over-represented in the group that was unsure and slow to make judgments (the "I-don't-knows" group).
It turned out that the "I-don't-knows" group was the best performing group in the game.

# The availability heuristic explains why individual investors overestimate risk by giving too much weight to recent bad news reports
http://www.bloombergview.com/articles/2016-04-29/the-mistake-that-separates-most-traders-from-the-pros
Robert Shiller has been polling investors for 26 years, asking them to estimate the likelihood of a severe crash (daily decline of at least 12.82%) over the next six months. 
The mean investor estimate over those 26 years has been 19%, and the average 10%, while the actual probability is closer to about 1%.
Goetzmann, Shiller, and Kim scanned news stories for both positive and negative words, and found that investors gave excessive weight to the most recent information in deciding on the probability of a crash. 
They found that when media reports contain more negative news, then individual investors provide a significantly higher estimate of crash risk. 
Individual investors are also much more influenced by negative news than by positive news. 
Positive words in media reports (such as "advance" or "boom") had no impact on individual investors estimates of crash risk. 
But institutional (professional) investors were not influenced by news at all.

# Berkeley J. Dietvors: algorithm aversion causes investors to choose human manager over a superior statistical algorithm
http://opim.wharton.upenn.edu/risk/library/WPAF201410-AlgorthimAversion-Dietvorst-Simmons-Massey.pdf
http://blog.alphaarchitect.com/2015/07/29/algorithm-aversion-why-people-dont-follow-the-model/
http://blog.alphaarchitect.com/2016/04/21/will-grove-post/
People more quickly lose confidence in algorithmic forecasters than in humans after seeing them both make the same mistake.
Grove study "Clinical Versus Mechanical Prediction: A Meta-Analysis" shows that algorithms are more accurate than humans, but humans don't trust algorithms anyway.

# Jim O'Shaughnessy behavioral finance book What Works on Wall Street: humans less reliable than algos
http://jimoshaughnessy.tumblr.com/post/127484251129/the-unreliable-experts-getting-in-the-way-of
Human experts perform worse than quantitative models because the don't apply their own ideas properly - they don't trust simple rules, even though they know they work. 
Quantitative models outperform human experts because they consistently apply the same rules without doubting them.
Daniel Kahneman studied cognitive biases: over-confidence, optimism, hindsight, illusion of control. 
Hindsight bias makes it appear that events that the best informed experts did not anticipate often appear almost inevitable after they occur.
Predictions can be made through either intuitive (clinical ), or quantitative (statistical) methods, or a combination of them.
In the intuitive methods, predictions are made based on personal knowledge, experience, and common sense. 
Heuristics are "rules of thumb" which forecasters can use to make decisions or forecasts.
The quantitative method doesn't rely on heuristics nor subjective judgments.
The quantitative method uses relationships between empirical data and the desired outcomes to reach conclusions. 
Paul Meehl 1954 study of quantitative prediction versus intuitive prediction. 
Meehl reviewed 20 studies comparing clinical and statistical predictions for three things: academic success, response to electric shock therapy, and criminal recidivism. 
Meehl found that simple quantitative models outperformed the human judges. 
In predicting academic success in college a model using just high school grade point average and the level attained on an aptitude test (such as the SAT) outperformed the judgment of admissions officers at several colleges. 
Robyn Dawes in his book House of Cards also found quantitative models to be superior. 
L.R. Goldberg devised a simple model using the results of the Minnesota Multiphasic Personality Inventory (MMPI), to distinguish between neurosis and 
psychosis of patients. 
The model achieved a success rate of 70%, while no human expert could achieve a success ratio of 67%. 
Humans don't like to follow models: even when human judges were given more information than the models and were given the result of the models before being asked for prediction, the quantitative models still beat the human judges.
A Wall Street Journal article from September 16, 2004 pointed out that blindly following Value Line's stock picks vastly outperformed the Value Line mutual fund, which aimed to follow Value Line's stock picks, but failed to stick to them.
Humans don't utilize base rate information if there is descriptive data.
Humans prefer descriptive data to impersonal statistics. 

# Behavioral finance papers showing that stock markets are not efficient
http://www.bloombergview.com/articles/2015-06-09/finance-has-caught-on-to-behavioral-economics
Robert Shiller famous 1981 paper shows that stock prices fluctuate more than fundamentals warrant.
Richard Thaler 1985 paper shows that stock prices over-react.
Kelly Shue and Stefano Giglio paper shows that stock prices of merger targets
don't rise gradually as merger becomes more likely merger after announcement.
After a merger announcement the stock price of merger target should grow 
gradually as time passes after the merger announcement, since it slowly becomes 
more likely the merger will be finalized.
But in reality stock prices grow with time much less than they ought to. 



###########
### blogs

# blog of blogs on quant trading and research
http://quantocracy.com/

# blog of blogs on stats research
http://www.statsblogs.com/

# Jonathan Kinlay - runs systematic hedge fund - good but no code
http://jonathankinlay.com/

# Cliff Asness AQR
https://www.aqr.com/cliffs-perspective/

# Eric Falkenstein economist, critic of CAPM, advocate of low volatility investing
http://www.efalken.com/index.html
http://falkenblog.blogspot.com/
http://www.efalken.com/papers/index.html
# Eric Falkenstein is a critic of Nassim Taleb and Taleb antifragile book
http://falkenblog.blogspot.com/2012/11/taleb-mishandles-fragility.html
http://www.efalken.com/papers/Taleb2.html

# QUSMA technical trading blog, mostly C# plus MATLAB, Octave, R, and C++
http://qusma.com/
https://github.com/qusma
# software: QPAS performance analysis for Interactive Brokers; QUSMA Data Management System
http://qusma.com/software/

# Dave Giles Professor of Economics - good Econometrics time series
http://davegiles.blogspot.com/
http://web.uvic.ca/~dgiles/

# Pawel Lachowicz Python quant
http://www.quantatrisk.com/

# Thiago Martins Bitcoin and risk quant Stat PhD candidate and Data Scientist at Yahoo!
http://tgmstat.wordpress.com/
https://github.com/tmartinx

# value investing - no code
http://investorfieldguide.com/

# Larry Takeuchi - good deep learning paper
http://econometrical.com/

# Michael Harris sells software for systematic trading
http://www.priceactionlab.com/
http://www.priceactionlab.com/Blog/

# Berkeley guy good algorithmic trading and behavorial economics - nothing since 2012
http://www.curatedalpha.com/
http://www.curatedalpha.com/category/behavorial-economics/
http://www.curatedalpha.com/popular-posts/
http://www.curatedalpha.com/2011/nassim-taleb-minimal-exposure-to-the-media-as-a-guiding-principle/
http://www.curatedalpha.com/2011/curated-interviews-from-quants-algorithmic-trading-and-high-frequency-trading-from-reddits-iama-part-1/
http://www.curatedalpha.com/2012/the-lure-of-active-management/

# Mark Buchanan econophhysics
http://physicsoffinance.blogspot.com/
http://physicsoffinance.blogspot.co.uk/
http://physicsoffinance.blogspot.com/2011/08/algorithmic-trading-positive-side.html
http://physicsoffinance.blogspot.com/2013/07/thinking-again-about-time.html
http://physicsoffinance.blogspot.com/2012/10/why-diversification-doesnt-work.html
http://physicsoffinance.blogspot.com/2011/12/power-laws-in-finance.html

# Arthur Charpentier: funny math and stats
http://freakonometrics.hypotheses.org/
Charpentier Bayesian Analysis Frequentist Hypothesis Testing.pdf

# quant trading and research articles - most free - some premium
https://www.cxoadvisory.com/

# Things That Make You Go Hmmm... excellent global market analysis
http://www.mauldineconomics.com/ttmygh

# junior quant advice
https://www.quantnet.com/

http://quant.stackexchange.com/
# Testing Valuation, Size and Momentum
http://quant.stackexchange.com/questions/8757/testing-valuation-size-and-momentum-proprietary-factors-from-1988-2013-no-ev

http://quant.stackexchange.com/questions/8838/rank-financial-instruments-by-price-movement-stability-and-outliers

http://quant.stackexchange.com/questions/8827/example-code-for-gauge-invariance-geometry-and-arbitrage-paper

http://www.automated-trading-system.com/

http://www.aphysicistinwallstreet.com/

http://intelligenttradingtech.blogspot.com/

http://www.codusoperandi.com/posts/why-i-quit-algorithmic-trading-to-do-web-startups

# econometric charts
http://soberlook.com/

# Jason Strimpel - not active
http://blog.jasonstrimpel.com/

# very weak
http://www.opentrades.nl/

# Victor Niederhoffer Scientific Method In Finance
http://www.dailyspeculations.com/wordpress/

# Eran Raviv
http://eranraviv.com/category/blog/finance/



###########
### blogs trading & websites: screens, charts, strategies, and backtests

# Stuart Reid Python programmer machine learning excellent
http://www.turingfinance.com/
# Quant trader using Python
http://www.stuartreid.co.za/
# links
http://www.stuartreid.co.za/interesting-links/
http://www.stuartreid.co.za/computational-finance-blog/
http://www.stuartreid.co.za/algorithmic-trading-system-requirements-post/

# James Picerno financial journalist using R
http://www.capitalspectator.com/
http://seekingalpha.com/author/james-picerno
https://gist.github.com/jpicerno1
https://github.com/jpicerno1

# Sebastien Bossu equity derivatives hedge fund manager, book author
http://sbossu.strikingly.com/

# Quantitative Finance Collector by Biao Guo Assistant Professor, School of Finance, Renmin University, China
http://www.mathfinance.cn/

# ETF screens and charts, strategies with backtests - some free - most premium
http://etfreplay.com/
http://etfreplay.com/blog/

# database of trading strategies with backtests - some free - most premium
http://www.quantpedia.com/

# stock, FX, futures screens and charts - some free - most premium
http://finviz.com/

# ETF and mutual fund trading strategies - cherry-picking from "Retired Strategies"
http://www.marketsci.com/

# Salil Mehta Bayes alpha analysis: distinguish skill from luck
http://statisticalideas.blogspot.com/2014/02/forever-elusive-alpha.html
http://www.nytimes.com/2014/04/06/business/the-oracle-of-omaha-lately-looking-a-bit-ordinary.html

# Jeff Swanson lots of conventional technical trading strategies using VIX, RSI, etc.
http://systemtradersuccess.com
http://systemtradersuccess.com/how-to-identify-algorithmic-trading-strategies/
# RSI strategy
http://systemtradersuccess.com/rsi-and-how-to-profit-from-it/
# Combining RSI And VIX
http://systemtradersuccess.com/vix-rsi/
http://systemtradersuccess.com/inverting-the-rsi-and-vix-strategy/
# Connors 2-Period RSI Update For 2013
http://systemtradersuccess.com/connors-rsi-update-for-2013/
# Improving Moving Average Crossover strategy
http://systemtradersuccess.com/improving-the-moving-average-crossover-system/
# Overnight Edge strategy
http://systemtradersuccess.com/overnight-edge/

# Global Market Rotation Strategy
http://logical-invest.com/
http://logicalinvest.wordpress.com/
https://www.quantopian.com/posts/global-market-rotation-strategy-buggy-implementation
http://seekingalpha.com/article/1622832-a-global-market-rotation-strategy-with-an-annual-performance-of-41_4-percent-since-2003?source=intbrokers_regular

# Sector Rotation trading using Credit Markets
http://www.atraderjournal.com/2013/04/sector-rotation-trading-using-credit_27.html
http://capitalcontext.com/

# David Lucca: Market Timing FOMC Calendar Strategy
http://www.zerohedge.com/news/2014-07-03/most-bizarre-market-timing-chart-ever
http://www.zerohedge.com/news/chart-year-fed-has-doubled-sp-admits-fed
http://libertystreeteconomics.newyorkfed.org/2012/07/the-puzzling-pre-fomc-announcement-drift.html#.U7ai6_ldWYI
http://online.wsj.com/articles/heard-on-the-street-making-money-with-the-fed-dont-get-mad-get-even-1404330574
http://faculty.haas.berkeley.edu/morse/research/papers/cieslakMorseVissingJorgensen.pdf

# Stephen R. Rush - Probable Pattern - UConn PhD student and R-fin 2014 presenter about VPIN
http://stephenrush.wordpress.com/
http://stephenrush.wordpress.com/code/
http://stephenrush.wordpress.com/open-source/
https://gist.github.com/ProbablePattern
# VPIN R code
https://gist.github.com/ProbablePattern/c46e4fb12bf758b99b03

# Internal Bar Strength IBS Mean Reversion
http://qusma.com/2013/09/11/paper-the-ibs-effect-mean-reversion-in-equity-etfs/
http://qusma.com/2012/11/06/closing-price-in-relation-to-the-days-range-and-equity-index-mean-reversion/
http://intelligenttradingtech.blogspot.com/2013/01/ibs-reversion-edge-with-quantshare.html
http://intelligenttradingtech.blogspot.com/2013/04/ibs-stingray-reversion-intuition-using.html
http://adaptivetrader.wordpress.com/2012/12/28/cumulative-ibs-indicator/

# Algo Trading and FX Market Efficiency
http://libertystreeteconomics.newyorkfed.org/2014/03/has-automated-trading-promoted-efficiency-in-the-fx-spot-market-.html#.U7ayD_ldWYI

# Matthieu Lestel PerformanceAnalytics contributor
matthieu.lestel@gmail.com
matthieu.lestel@lpredatoranalytics.com
http://www.lpredatoranalytics.com/
https://github.com/LitPepito



###########
### portfolio economics research

# best journals:
Journal of Financial Economics
Journal of Finance
Review of Financial Studies
American Economic Review
Journal of Political Economy
https://www.dimensional.com/famafrench/questions-answers/qa-how-do-you-decide-which-research-papers-to-read.aspx

# ARPM Attilio Meucci bootcamp (Python and Matlab) associated with Kepos (Mark Carhart)
http://www.arpm.co/
http://www.arpm.co/paper-series/

# Isaac bar Aha: "put a third in land, a third in merchandise, and a third in cash"
http://finance.yahoo.com/news/naive-diversification-vs-optimization-163550886.html

# Gary Antonacci dual momentum strategy
http://www.dualmomentum.net/
#  Global Equities Momentum (GEM)
http://www.optimalmomentum.com/gem_trackrecord.html

# Adam Butler GestaltU - Adaptive Asset Allocation - Tactical Asset Allocation
http://gestaltu.com/
http://gestaltu.com/skew/
http://www.investresolve.com/

# Wouter J. Keller - Momentum Flexible Asset Allocation
http://www.wkeller.nl/
Keller Momentum Markowitz Asset Allocation.pdf

# Enrico Schumann NMOF package minimum-variance portfolios
http://enricoschumann.net/

# Victor DeMiguel London Business School Shrinkage Estimators Portfolio Optimization
http://faculty.london.edu/avmiguel/

# Olivier Ledoit Shrinkage Estimators Portfolio Optimization
http://www.ledoit.net/
http://studdridge.com/profile/

# Maxim Golts Fidelity Investments - Constrained Portfolio Optimization
https://www.linkedin.com/in/maxgolts

# portfolio optimization construction shrinkage
http://quant.stackexchange.com/questions/44/what-methods-do-you-use-to-improve-expected-return-estimates-when-constructing-a
http://quant.stackexchange.com/questions/10101/portfolio-optimization-shrinkage-of-covariance-matrix-when-data-is-available

# Cluster Random Subspace portfolio optimization - cluster, sample, optimize
https://cssanalytics.wordpress.com/2014/11/04/cluster-random-subspace-method-for-portfolio-management/
https://cssanalytics.wordpress.com/2014/11/05/cluster-random-subspace-a-process-diagram/

# qmj factor package David Kane Hutchin Hill + Anthoney Tsou
https://github.com/anttsou/qmj
https://github.com/anttsou/qmjdata
https://www.linkedin.com/in/anthoney-tsou-109915b7

# Portfolio Particle Swarm Optimization
http://www.turingfinance.com/portfolio-optimization-using-particle-swarm-optimization/

# Q Group Treynor Prize, Institute for Quantitative Research in Finance
http://www.q-group.org/

# James B. Heaton
http://www.bartlit-beck.com/lawyers-JamesHeatonIII.html

# Jan Hendrik Witte
http://www.bellmanoptimality.com/
https://www.linkedin.com/in/jhwitte

# Nicholas Polson at Chicago Booth
http://www.chicagobooth.edu/faculty/directory/p/nicholas-polson
http://faculty.chicagobooth.edu/nicholas.polson/

# Nicolas Baradel works for Bruno Bouchard - Limit Order Market Making
http://www.nicolasbaradel.fr/
http://www.crest.fr/pagesperso.php?user=3399
# PGM Solutions: create GUI from R scripts
http://www.pgm-solutions.com/



###########
### asset pricing forecasting efficient market hypothesis

# Amit Goyal at Lausanne

# Cowles-Jones test for randomness of time series
Liesenfeld Forecasting Asset Returns Cowles-Jones Test.pdf

# John Y. Campbell professor at Harvard
http://scholar.harvard.edu/campbell/home

# Piotr Orlowski high frequency option models
piotr.orlowski@usi.ch
piotrek.orlowski@gmail.com
http://www.people.usi.ch/orlowskp/
http://www.people.usi.ch/orlowskp/code.html
https://github.com/piotrek-orlowski

# Paul Schneider professor at Universita della Svizzera italiana
paul.schneider@usi.ch
http://search.usi.ch/projects/719/Trading-Asset-Pricing-Models
http://www.people.usi.ch/trojanif/
https://sites.google.com/site/paulgschneider/home/papers
http://search.usi.ch/people/27026b456424b7decb40fbe1bc4c9c7c/Schneider-Paul

# Ian Martin at LSE, stock forecasting using options
http://personal.lse.ac.uk/martiniw/

# Lucas Trees Asset Pricing Model
http://quant-econ.net/jl/lucas_model.html



###########
### trading systematic research

# Research archives
http://arxiv.org/archive/q-fin
# SciRate network shows highest ranked arXiv.org papers
https://scirate.com/

# SSRN top papers
http://ssrn.com/en/
http://ssrnblog.com/

http://quantpapers.com/

# list of source materials on systematic trading
https://www.quora.com/topic/Algorithmic-Trading
http://quant.stackexchange.com/questions/15013/what-are-the-textbooks-used-to-teach-quantitative-trading-at-universities
https://www.quora.com/Algorithmic-Trading/What-are-some-good-tutorials-for-backtesting-in-R

# David H. Bailey: Mathematicians Against Fraudulent Financial and Investment Advice (MAFFIA)
http://www.davidhbailey.com/
# Bailey blog
http://www.financial-math.org/
http://www.financial-math.org/blog/
# Spigot algorithm
http://en.wikipedia.org/wiki/Spigot_algorithm
http://en.wikipedia.org/wiki/Bailey%E2%80%93Borwein%E2%80%93Plouffe_formula

# Marcos López de Prado - Backtesting Overfitting Cross-validation
lopezdeprado@gmail.com
http://www.quantresearch.info/
http://www.math.columbia.edu/mafn/program/practitioners-seminar-2014/
# Backtest Overfitting
http://www.financial-math.org/blog/2014/04/faqs-on-backtest-overfitting/
http://www.bloomberg.com/news/2014-04-11/computer-models-often-use-unsound-math-researchers-say.html

# Jon Borwein MAFFIA member CARMA leader
http://www.financial-math.org/blog/
https://www.carma.newcastle.edu.au/expmaths/

# Jaffray Woodriff QIM - ensemble learning
http://www.institutionalinvestor.com/article.aspx?articleID=2361672#.Uyz21vldU-V
http://transparentsimplicity.com/wordpress/

# Jaffray Woodriff using random data as a benchmark
http://qusma.com/2013/01/22/doing-the-jaffray-woodriff-thing/

# Interview with Tim Wong from AHL
http://www.futuresindustry.org/futures-industry.asp?iss=210&a=1553

# transaction costs in simulated market environment
http://quant.stackexchange.com/questions/3573/how-to-account-for-transaction-costs-in-a-simulated-market-environment
http://quant.stackexchange.com/questions/1264/how-to-simulate-slippage?rq=1
http://quant.stackexchange.com/questions/3599/how-should-i-include-the-bid-ask-spread-as-a-transaction-cost-in-a-backtest?rq=1
http://www.wsj.com/articles/SB10000872396390444024204578044293008576204
http://www.investopedia.com/articles/trading/121701.asp
http://www.investopedia.com/articles/investing/082213/how-calculate-bidask-spread.asp

# systematic quantitative market making group in Chicago
https://systematicedge.wordpress.com/
https://systematicedge.wordpress.com/2013/10/14/random-subspace-optimization-max-sharpe/



###########
### utility Kelly criterion

# Edward O. Thorp
http://edwardothorp.com/id9.html
# The Kelly Criterion In Blackjack Sports Betting, And The Stock Market
http://edwardothorp.com/sitebuildercontent/sitebuilderfiles/KellyCriterion2007.pdf

http://tgmstat.wordpress.com/2014/01/22/declining-marginal-utility-and-the-logarithmic-utility-function/

### Kelly criterion intertemporal portfolio choice

# Kelly criterion: expected net winnings divided by odds (which is variance)
http://www.quantstart.com/articles/Money-Management-via-the-Kelly-Criterion
http://quant.stackexchange.com/questions/7197/kelly-criterion-and-sharpe-ratio

# intertemporal portfolio choice
http://en.wikipedia.org/wiki/Intertemporal_portfolio_choice
http://en.wikipedia.org/wiki/Merton%27s_portfolio_problem

# Shlok Datye: Kelly Criterion and Optimal-f
http://shlok.is/thesis
Shlok Kelly Optimal-f Criteria.pdf
# Kelly Criterion and Optimal-f
https://alphaism.wordpress.com/2012/04/13/testing-kelly-criterion-and-optimal-f-in-r/



### momentum trading

# Jack Vogel Alpha Architect analysis of momentum investment strategies
http://blog.alphaarchitect.com/2015/11/16/how-the-number-of-firms-and-holding-periods-affect-momentum-etfs/
http://www.dualmomentum.net/2014/11/individual-stock-momentum-that-dog-dont.html
https://www.researchaffiliates.com/Our%20Ideas/Insights/Fundamentals/Pages/448_How_NOT_to_Wipe_Out_with_Momentum.aspx

# NYFed Treasury momentum trading

http://libertystreeteconomics.newyorkfed.org/2014/05/can-investors-use-momentum-to-beat-the-us-treasury-market.html#.U7apIfldWYI

http://www.aqrindex.com/AQR_Momentum_Indices/Momentum_Research/Content/default.fs
http://physicsoffinance.blogspot.com/2014/04/how-to-consistently-beat-market-follow.html
https://medium.com/the-physics-of-finance/8c61c714e8fd

# Kent D. Daniel - momentum crashes
http://www.kentdaniel.net/

# momentum papers
# Two centuries of trend following
http://arxiv.org/abs/1404.3274
# Lee Fundamental Data Improving Momentum Strategy
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1590460
Lee Fundamental Data Improving Momentum Strategy.pdf

# Getmansky and Lo paper reviews studies of hedge fund performance
http://www.bloombergview.com/articles/2015-08-20/are-hedge-funds-fake-
Fung and Hsieh paper shows that returns of an actively managed fund that can 
perfectly predict price trends resembles a payoff of a lookback straddle. 
They call the optimal trend-following strategy of buying lookback straddles the 
"Primitive Trend-Following Strategy". 

# Trend-following is Dead
http://www.priceactionlab.com/Blog/2015/11/trend-following/
http://www.followingthetrend.com/2015/11/apparently-trend-following-just-died-again/

http://www.philosophicaleconomics.com/2016/01/movingaverage/


# quant blog - stale now
https://quantivity.wordpress.com/
# momentum review - good
http://quantivity.wordpress.com/2012/10/25/implicit-momentum-bias/

# Larry Swedroe: Myths About Momentum
http://seekingalpha.com/article/2226863-myths-about-momentum-part-i
http://seekingalpha.com/article/2229023-myths-about-momentum-part-ii

# Quantpedia Momentum and Style Rotation Effect
http://www.quantpedia.com/Blog/Details/sample-strategy-5-momentum-and-style-rotation-effect

# Andrew Swanscott bettersystemtrader
http://bettersystemtrader.com/
http://bettersystemtrader.com/sharpe-ratio-right-answer-wrong-question/

# Laurent Bernut Alpha Secure Capital
http://alphasecurecapital.com/
# There are only two types of strategies: mean reversion or trend following
# The best risk metric for mean reverting strategies is the tail ratio
# A simple rule of thumb is to redeem mean reverting strategies if losses are below 0.4 of turnover
# Trend following strategies have tail ratios ranging from 3 to 10. 
# Winners are much bigger than losers. 
# So, tail ratio is meaningless for trend followers.
# Trend following risk is not in the tails, but in the aggregates
# Risk of trend following strategies is best measured by the ratio of profits over 
# losses. The risk metric for trend following strategies is therefore: Gain to Pain Ratio 
http://alphasecurecapital.com/regardless-of-the-asset-class-there-are-only-two-types-of-strategies/

# Characteristics of mean reversion strategies
https://www.quora.com/Under-what-market-conditions-does-mean-reversion-work-better-than-trend-following



###########
### high frequency trading

# high frequency trading tutorial
http://quant.stackexchange.com/questions/147/what-are-the-key-risks-to-the-quantitative-strategy-development-process/150
http://quant.stackexchange.com/questions/1501/which-approach-dominates-mathematical-modeling-or-data-mining/1505
http://www.allaboutalpha.com/blog/2011/09/06/the-secrets-of-high-frequency-trading/
http://www.zerohedge.com/news/2014-04-03/marc-cubans-primer-hft-idiots
http://blogmaverick.com/2014/04/03/the-idiots-guide-to-high-frequency-trading/
https://news.ycombinator.com/item?id=3894302

# high frequency market making
http://quant.stackexchange.com/questions/1274/how-can-we-reverse-engineer-a-market-making-algorithm-hft

# high frequency market making inventory problem solution
http://arxiv.org/abs/1105.3115

# Kipp Rogers: patterns in orders provide profitable trading strategies
https://mechanicalmarkets.wordpress.com/2015/04/30/market-data-patterns-order-anticipation-and-an-example-trading-strategy/

# Eric Scott Hunsader founded Nanex
http://www.marketwatch.com/story/this-man-wants-to-upend-the-world-of-high-frequency-trading-2016-02-02



###########
### futures trading

# USDX ICE U.S. Dollar Index currency futures
expiries March, June, September, December
https://www.theice.com/products/194/US-Dollar-Index-Futures
http://www.barchart.com/commodityfutures/US_Dollar_Index_Futures/DX
http://www.barchart.com/charts/futures/DXY00

# futures and commodities trading
http://www.investopedia.com/exam-guide/series-3/studyguide/chapter7/bull-bear-spreads.asp
https://www.khanacademy.org/economics-finance-domain/core-finance/derivative-securities/forward-futures-contracts/v/backwardation-bullish-or-bearish
Contango is a state where the futures price is higher than the spot price.
A contango is normal for non-perishable commodities that have a cost of carry, and for stable or bear markets.
Backwardation is a state where the spot price is higher than the futures price.
Backwardation typically occurs during bull markets.
In contango the futures curve is upward sloping, and in backwardation it's downward sloping.
The shape of the futures curve in bull and bear markets reflects the market's expectations of price reversion to the mean.
If prices are high today, they are expected to revert lower (backwardation), and vice versa in contango.
In contango, futures bull spread trades (buy nearby contract and sell distant contract) earn a positive carry, reflective of the cost of carry of the physical commodity.
Volatility futures behave reversely to stock futures, since a bull market in volatility represents a bear market for stocks.
Volatility futures are upward sloping in stock bull markets (low volatility), and downward sloping in stock bear markets (high volatility).



###########
### volatility VIX trading

# volatility traders
http://largecaptrader.com/
http://www.tradingtheodds.com/
# Double Digit Numerics DDN Volatility Risk Premium Strategy
http://www.tradingtheodds.com/2014/11/ddns-volatility-risk-premium-strategy-revisited-2/

# volatility ETFs VXX and XIV strategy tracking
http://volatilitymadesimple.com/
http://volatilitymadesimple.com/a-momentum-rotation-strategy-for-trading-vix-etps/

# ETF and mutual fund trading strategies, including volatility ETFs VXX and XIV
http://www.marketsci.com/
http://www.marketsci.com/strategy.VT.html

# VXX shares outstanding is a sentiment indicator for hedging demand, positively correlated with stock prices
http://www.zerohedge.com/news/2016-04-27/tom-mcclellan-has-stark-warning-can-possibly-end-well

# Chrilly Donninger momentum, ETF, VIX strategies
http://www.godotfinance.com/
http://www.godotfinance.com/workingpapers/

# Christopher Cole Artemis Vega Fund Long VIX Volatility making great returns
http://www.zerohedge.com/news/2015-09-02/your-crisis-alpha-action-hedge-fund-made-15-yesterday-market-tumbled

# VIX strategies - stale
http://vixandmoreevals.blogspot.com/

# VIX short futures strategy +56% ytd 2015
http://www.zerohedge.com/news/2015-08-02/most-successful-trade-last-decade
http://www.zerohedge.com/news/2015-08-03/vix-etf-surges-record-low-crushing-contango-cruisers

# August 24th VIX crash VIX inverse XIV short vol strategy has huge negative convexity
http://www.zerohedge.com/news/2015-10-20/how-entire-short-volatility-etf-complex-could-be-wiped-out-overnight
XIV rebalancing creates posiitive feedback loop in VIX, because when VIX rises then XIV needs to buy VIX.  Thus XIV has huge negative convexity. 
Example was during the August 24th crash, when SPX futures opened limit down and VIX futures didn't open for 30 minutes. 
When VIX opened it was 25 points higher at 53.29, before falling to 28 intra-day, then rebounding to 40.74 by the close, with the S&P 500 index down -3.9%.
This was historically record breaking volatility-of-volatility despite relatively mild historical declines in the S&P 500 index.

# VIX strategies
http://statisticalideas.blogspot.com/2015/06/volatility-products-prayer.html
http://statisticalideas.blogspot.com/2015/02/volatility-product-hitmen.html
http://statisticalideas.blogspot.com/2015/02/volatility-product-proselytizers.html

# VIX strategies Jonathan Kinlay
https://www.linkedin.com/pulse/great-year-volatility-relative-value-jonathan-kinlay
https://www.linkedin.com/pulse/daytrading-volatility-etfs-jonathan-kinlay
# use data at frequencies of 1-5 minutes, trading at latencies of around 1 millisec,
# derive signals from other markets: VXX-ES-TY
https://www.linkedin.com/pulse/making-money-high-frequency-trading-jonathan-kinlay

# options strategies but without Java code for backtesting
http://dtr-trading.blogspot.ca/



###########
### lectures

# Daniel Bloch ebook Quantitative Portfolio Management
http://quantfin.eu/

# Bloomberg Labs Seminars
http://www.bloomberglabs.com/
http://www.bloomberglabs.com/quantitative-finance/events/

# Marco Avellaneda NYU Courant and Finance Concepts, previous Capital Fund Management Nimbus Fund
http://www.math.nyu.edu/faculty/avellane/
http://quantivity.wordpress.com/2011/05/28/avellaneda-lectures-model-risk/

# Eric Zivot Course Slides
http://faculty.washington.edu/ezivot/econ424/424notes.htm
https://www.coursera.org/course/compfinance

# Eric Zivot on Coursera: Computational Finance and Financial Econometrics
https://www.coursera.org/course/compfinance
# Computational Investing Part I
https://www.coursera.org/course/compinvesting1
# Financial Engineering and Risk Management
https://www.coursera.org/course/fe1
https://www.coursera.org/course/fe2

# DataCamp & rcademy - Jonathan Cornelissen (works with Boudt)
https://www.datacamp.com/

# Jacek Suda - Paris School of Economics - time series and Kalman
http://www.jaceksuda.com/index.html
http://www.jaceksuda.com/teach/pseFall2012/index.html



###########
### systematic funds

# Two Sigma was founded in 2001 by David Siegel, the former CTO at Tudor, who received a PhD in computer science from MIT, and John Overdeck from D.E. Shaw, who was an International Mathematics Olympiad Silver Medalist in 1986.

# Pension Partners Beta Rotation Index
https://pensionpartners.com/

# Sang Lucci Trading - options courses
http://www.sanglucci.com/
http://www.bloomberg.com/news/2014-05-08/traders-scan-brains-in-hunt-for-profit-enlarging-pill.html

# Spitznagel funds: Universa, Empirica Capital with Nassim Taleb
http://dealbook.nytimes.com/2014/11/24/bear-going-vs-the-bulls-still-profits/
http://www.zerohedge.com/news/2014-11-25/bearish-mark-spitznagel-profiting-strongly-2009-warns-only-so-much-debt-economy-can-

# Binatix deep learning trading firm
http://recode.net/2014/09/10/introducing-binatix-a-deep-learning-trading-firm-thats-already-profitable/
http://www.binatix.com/
http://www.binatix.com/technology.html



###########
### investing

# Piotroski fundamental F-score for value stocks flopped in 2014
http://triblive.com/business/johndorfman/7431505-74/stocks-score-percent#axzz3rgsQJbul

# Partha Mohanram G-score for growth stocks
http://www.businessinsider.com/glamour-stocks-avoiding-falling-stars-using-mohanrams-g-score-2011-7

# Sam Stovall is chief investment strategist at S&P
# S&P Capital IQ customer page: STARS, Fair Value and Quality Rankings for stocks
http://www.advisor.marketscope.com/SP/msa/login.html#historyId0
# S&P Capital IQ MarketScope
http://www.standardandpoors.com/products-services/marketscope/en/us
# S&P's Dividend Aristocrats
http://www.moneyshow.com/articles.asp?aid=GURU-33694
# Fidelity: Capital IQ ETF Research
http://research2.fidelity.com/fidelity/research/reports/release2/Research/ETFStandardAndPoors.asp
# Capital IQ was founded in 1999
http://en.wikipedia.org/wiki/S%26P_Capital_IQ
# S&P Capital IQ 5-STAR ranked stocks have produced 1,387% total return since 1992, versus 542% for the S&P 500 index
http://finance.yahoo.com/news/p-capital-iq-equity-research-150000672.html
# S&P SDY and NOBL ETF's performance statistics
https://finance.yahoo.com/q/rk?s=SDY+Risk
https://finance.yahoo.com/q/rk?s=NOBL+Risk

# google search
S&P Capital IQ STARS ranked stock performance -site:capitaliq.com -site:standardandpoors.com -site:marketscope.com

# Betterment expert selected ETF Investing with automatic rebalancing
https://www.betterment.com

# Wealthfront diversified ETF Investing with automatic rebalancing
# Modern Portfolio Theory investment methodology: maximize returns through optimal asset mix,
https://www.wealthfront.com

# Value Investing
http://www.validea.com/home/home.asp



###########
### investing systematic

# Wesley R. Gray book Quantitative Value
http://empiritrage.com/
http://turnkeyanalyst.com/
http://alphaarchitect.com/blog
http://blog.alphaarchitect.com/category/architect-academic-insights/
http://abnormalreturns.com/qa-with-wesley-gray-co-author-of-quantitative-value/

# Mebane Faber
http://www.mebanefaber.com/
http://www.mebanefaber.com/reading-list/
http://mebfaber.com/2013/12/04/square-root-of-f-squared/
http://f-squaredinvestments.com/alphasector/alphasector-premium/
F-Squared sector ETF strategy

# Mebane Faber Tactical Asset Allocation
http://www.bloomberg.com/news/articles/2015-09-01/the-most-downloaded-ssrn-paper-ever-is-all-about-market-timing

# David Varadi Dynamic Asset Allocation
http://cssanalytics.wordpress.com/
http://www.bsam.com/
http://www.bsam.com/research/
https://www.linkedin.com/pulse/think-mpt-doesnt-work-you-probably-using-wrong-way-david-varadi?trk=hp-feed-article-title-comment

# Alex Chinco finance prof at Urbana-Champaign uses R: 
http://www.alexchinco.com/
http://business.illinois.edu/facultyprofile/faculty_profile.aspx?ID=15101
# R code to replicate main results in Ang, Hodrick, Xing, and Zhang (2006)
Ang Idiosyncratic Volatility Anomaly.pdf
https://gist.github.com/alexchinco/d58ebd7750904db1b94c
https://gist.github.com/alexchinco
https://github.com/alexchinco

# low idiosyncratic volatility anomaly in Ang, Hodrick, Xing, and Zhang (2006)
http://www.alexchinco.com/notes-ang-hodrick-xing-and-zhang-2006/
http://www.edhec-risk.com/latest_news/featured_analysis/RISKArticle.2013-05-22.1255?newsletter=yes

# F-Squared Investments Receives Wells Notice From SEC
http://online.wsj.com/articles/f-squared-investments-received-wells-notice-from-sec-1409768768
F-Squared says it uses mathematical models to generate signals to buy and sell among a range of ETFs, which track a variety of global stock and bond markets.

http://online.barrons.com/articles/SB50001424053111904780504580049961992594040
Managed portfolios are the hottest trend in asset management. 
Managed portfolios are similar to asset-allocation mutual funds built with low-cost, passive ETFs.
But advisors have very little track record.
Many of their managed portfolios are based on back tests and financial engineering. 
F-Squared Investments is the biggest, managing $28 billion.
F-Squared Investments admitted it overstated its past performance
Be sure your manager's track record is real. 
Windhaven Investment Management, with $19 billion, is second.
Good Harbor Tactical Core U.S. is a popular aggressive strategy, but is down by 10.2% as of midyear 2014. 
Good Harbor suffered when small-company stocks and other high-momentum sectors broke down in early 2014.
Good Harbor's CIO is Paul Ingersoll.

# Managed ETF performance has been dismal
http://online.barrons.com/articles/why-etf-managed-accounts-fail-1412404692
Separate "wrap" accounts with a five-year track record have underperformed.
Tactical strategies try to ride the market’s uptrends and avoid its downtrends by getting out before losses are too great. 
iSectors Post-MPT Growth Allocation, employs macro-economic signals to determine its allocations. 
Its 18.3% five-year annualized return beats 99% of its peers in the world allocation category, 
but still slightly lags the S&P 500’s 18.8% return. 
The market is up-down, up-down, and even when it rises, it goes up too quickly.
So almost all tactical strategies are getting in after the market’s already gone up.
They’re inadvertently buying high and selling low—just the opposite of what you want to be doing.

# iSectors offers a suite of 15 ETF-based asset allocation models
http://isectors.com/post-mpt-strategies/
http://finance.yahoo.com/news/investing-insights-isectors-utilizes-etfs-130043073.html
iSectors model optimizes the portfolio allocation among up to nine asset classes.  

# Dimensional Fund Advisors beats the market consistently
http://us.dimensional.com/
http://assetbuilder.com/investing/dfa_vs_vanguard
http://online.barrons.com/news/articles/SB50001424053111904742804579284652021894432
Dimensional's funds all operate on the same principles -- that it's hard to beat the market, and impossible to do it consistently, by stock-picking. There are, however, various factors that can be exploited to provide market-beating returns. 
Its process is simple and repeatable -- and yet no other firm has tried emulating it. When asked why, co-founder, chairman, and co-CEO David Booth, 67, draws a surprising analogy to Star Wars, and Luke Skywalker's inability to harness the power of the Force until his devotion was deep and unwavering.
Trade execution is critical to another factor that Fama and French added to their model more recently: Stocks that are falling tend to continue to fall, and those that are rising tend to continue to rise. "So you want to trade slowly," Booth says. "We are slow to trade most of the new names that have recently fallen into the value category, because they are negative-momentum stocks. We're also slow to sell positive-momentum stocks. We'll hold on for years."

# David Booth co-founded Dimensional Fund Advisors in 1981
http://en.wikipedia.org/wiki/David_G._Booth
Booth was a research assistant to Eugene Fama.
Dimensional manages about $360 bn and invests in small (low capitalization) and value (low price/book ratio) stocks and non-U.S. stocks. 
David Booth gave $300 mm to the University of Chicago's School of Business, now named after him.

# Jeremy Siegel: Stocks for the Long Run
http://www.riosmauricio.com/wp-content/uploads/2013/05/Siegel_Stocks-For-The-Long-Run.pdf
http://www.aaii.com/files/investorupdate/20140410.html
http://theguruinvestor.com/2014/04/11/the-moving-average-strategy-good-results-if-you-have-the-discipline/
http://www.raymondjames.com/assetadvice/FlexModel.aspx
http://www.ft.com/intl/cms/s/0/23c9f650-10c7-11e3-b5e4-00144feabdc0.html#axzz30r9n7OpV

# Eric H. Sorensen, CEO of PanAgora
# Edward E. Qian at PanAgora book: Quantitative Equity Portfolio Management
http://www.panagora.com/people/eric-sorensn/
# Decision Tree Approach (CART) to Stock Selection, The Journal of Portfolio Management, Fall 2000 (with Keith Miller).
http://www.iijournals.com/doi/abs/10.3905/jpm.2000.319781#sthash.luDp0VuS.dpbs

# Robert Shiller: Irrational Exuberance data and charts
http://www.irrationalexuberance.com/index.htm
# CAPE Shiller P/E currently stocks overvalued
http://blogs.ft.com/ft-long-short/2013/08/13/the-cape-of-less-hope/

# matching investors to hedge funds using psychological analysis of investors
http://www.news.getaltx.com/
http://getaltx.com/
http://imatchative.com/

http://www.trendarchitect.com/
http://www.trendarchitect.com/performance/
http://arcanecapital.autotradenow.com/cgi-perl/system85750979

http://www.spylon.com/

http://arcanecapital.autotradenow.com/
http://arcanecapital.autotradenow.com/about-this-site

# adaptivwealth Minimum Variance Portfolio plus price momentum
http://www.adaptivwealth.com/



###########
### sentiment indicators

# Goldman Sachs sentiment indicator has strong contrarian forecasting ability for stocks
http://www.bloomberg.com/news/articles/2016-09-12/goldman-we-ve-reached-maximum-bullishness-and-that-s-bad-news-for-u-s-stocks
Indicator levels above 90 or below 10 are have significant ability to forecast stocks returns over the following weeks.
Indicator levels above 90 forecast negative stocks returns of about -2% over the next two weeks.
Goldman Sachs sentiment indicator reached the maximum 100 level before the September 2016 selloff

# Suzy Moat - Future Orientation Index - Google searches predict market moves
http://www.wbs.ac.uk/about/person/suzy-moat/
http://www.suzymoat.co.uk/

# Tobias Preis - Google Sentiment Trends
http://www.tobiaspreis.de/
http://www.preis.co.uk/
# Future Orientation Index - Characterizing the Time-Perspective of Nations with Search Engine Query Data
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0095209
# Google Trends Can Predict The Market
http://www.forbes.com/sites/davidleinweber/2013/04/26/big-data-gets-bigger-now-google-trends-can-predict-the-market/
# Quantifying the Relationship Between Financial News and the Stock Market
http://www.nature.com/srep/2013/131220/srep03578/full/srep03578.html

# FinSentS News Sentiment Screener
http://portal.finsents.com/
https://www.quantopian.com/posts/market-sentiment-market-mood-finsents-signals-detection

# Gross National Happiness as Stock Market Return Predictor
# CXO Advisory: GNH has no predictivve ability
http://www.cxoadvisory.com/16248/sentiment-indicators/gross-national-happiness-as-stock-market-return-predictor/
http://socialtimes.com/facebook-happiness-index-linked-stock-market-behavior_b200303
http://www.sciencedirect.com/science/article/pii/S0167268114001735

# Twitter scraping option trading robots
http://www.slate.com/articles/business/moneybox/2015/04/bot_makes_2_4_million_reading_twitter_meet_the_guy_it_cost_a_fortune.html
Trading robots buy cheap options in a second after tweet.
Bloomberg and Dow Jones have designed direct robot news feeds.
bots-gone-wild hazard: in 2008 a 6-year-old news story about United Airlines 2002 bankruptcy somehow reappeared online, and got scraped by algos. 
United stock price plummeted by 76% within minutes, but recovered almost fully by end of day.

# iSENTIUM Daily Directional Indicators based on Twitter sentiment
http://www.isentium.com/
http://isentium.com/ddi.html

# Accern web media intelligence sentiment indicator by NYU student Anshul Vikram Pandey collaborating with Quantopian
http://accern.com/
http://accern.com/blog_single.php?imageid=32

# Twitter Decahose Firehose API sentiment indicators
https://gnip.com/
https://blog.gnip.com/
http://datasift.com/platform/datasources/twitter/
# Global Twitter Heartbeat 
https://www.sgi.com/go/twitter/

# ECB paper Twitter Gardenhose sentiment indicator better than Google
http://www.bloomberg.com/news/articles/2015-07-15/if-twitter-is-bullish-maybe-you-should-be-too
Twitter bullishness predicts changes in Google bullishness.
Twitter and Google bullishness are positively correlated to investor sentiment 
and lead investor sentiment surveys.
Twitter beats Google and both of them beat established sentiment surveys. 

# Sentiment scraping using Google spreadsheets
http://www.analyticbridge.com/profiles/blogs/how-to-transform-your-google-spreadsheet-into-an-opinion-mining



###########
### Books and Academic Papers

# Quantitative Finance papers database
http://arxiv.org/archive/q-fin

# Norvig and Russell: Artificial Intelligence: A Modern Approach
http://aima.cs.berkeley.edu/

# David Leinweber at Berkeley book Nerds on Wall Street
http://nerdsonwallstreet.com/
http://www.forbes.com/sites/davidleinweber/
http://www.forbes.com/sites/davidleinweber/2013/04/26/big-data-gets-bigger-now-google-trends-can-predict-the-market/
http://crd.lbl.gov/groups-depts/SDM/current-projects/cift
MarketMind
QuantEx



###########
### market and economic research

# NFP Non-farm Payrolls monthly report is a lagging indicator of economic activity
http://www.forexfraud.com/forex-articles/understanding-the-non-farm-payrolls-release.html
http://www.investopedia.com/university/releases/employmentsituation.asp

# Nassim Nicholas Taleb: Antifragile systems posses a convexity bias through tinkering (trial and error)
https://edge.org/conversation/nassim_nicholas_taleb-understanding-is-a-poor-substitute-for-convexity-antifragility
Convexity bias allows systems to gain from "disorder cluster", like volatility, uncertainty, disturbances, randomness, and stressors. 
Antifragile systems gain from uncertainty and they favor optionality. 
Convexity is easier to attain than knowledge.
Antifragile systems favor simplicity (premium for simplicity). 
Optionality works by negative information: by avoiding things that don't work, instead of choosing things that do work. 
This allows accidentally discovering things that do work. 



###########
### econophysics

# Mark Buchanan: the economy is a "complex system" - econophysics
https://medium.com/the-physics-of-finance/arrogant-physicists-do-they-think-economics-is-easy-b91a9e6ee184

# Alex Rosenberg and Tyler Curtain: Economics isn't a science like physics
http://opinionator.blogs.nytimes.com/2013/08/24/what-is-economics-good-for
Economic theory should be understood more on the model of music theory than Newtonian theory. 

# Mark Buchanan: Macroeconomics is just intuition and beliefs
https://medium.com/bull-market/economic-hot-air-87ecce480cdc
There’s very little real knowledge in economics on which arguments can be settled.
Paul Krugman and John Cochrane arguing viciously over things we just don’t know much about.
Ed Leamer: Our understanding of causal effects in macroeconomics is virtually nil, and will remain so. 
Dynamic Stochastic General Equilibrium are just "patterns and stories", not cause and effect.

# computer simulation of market shows discovery of money all on its own
http://www.bloombergview.com/articles/2013-06-05/what-to-do-when-the-invisible-hand-stops-working

# non-linear models needed because economy is complex adaptive system
http://rogerfarmerblog.blogspot.com/2015/04/new-solutions-to-old-problems.html
http://www.voxeu.org/article/stabilities-and-instabilities-macroeconomy

# Brian Arthur complexity economics econophysics
http://tuvalu.santafe.edu/~wbarthur/Papers/Comp.Econ.SFI.pdf

# minority games
http://physicsoffinance.blogspot.com/2012/02/minority-games.html
The market should tend to evolved toward a value of alpha where the predictability of prices is low but not zero, making it hard but not impossible to find patterns in the market.
http://arxiv.org/abs/physics/0608091

# Didier Sornette
http://www.er.ethz.ch/people/sornette
# Financial Crisis Observatory
http://www.er.ethz.ch/financial-crisis-observatory.html
# ETH Risk Center
http://www.riskcenter.ethz.ch/

# Felix Patzelt: Adaptive Control Dynamics. Bubbles, Jumps, and Scaling from Properly Anticipated Prices, Power Laws, Intermittency and Levy
http://www.neuro.uni-bremen.de/memberinfo/468/Felix%20Patzelt
# The Seesaw Game
http://seesaw.neuro.uni-bremen.de/

# Xavier Gabaix at NYU Stern - power laws, risk premia puzzles
http://pages.stern.nyu.edu/~xgabaix/

# Neil Johnson UMiami and book "Financial Market Complexity"
http://www.physics.miami.edu/~njohnson/

# Brice Dupoyet Florida International University
http://business.fiu.edu/faculty/faculty_experts.cfm?FlagDirectory=Display&User=dupoyetb
http://faculty.fiu.edu/~dupoyetb/
https://www.researchgate.net/profile/Brice_Dupoyet

# Jean-Philippe Bouchaud - Capital Fund Management
https://www.cfm.fr/

# Arthur Berd Macro Vol Strategies at Capital Fund Management
https://www.linkedin.com/in/arthurberd
http://www.risk.net/static/arthur-berd
# Journal of Investment Strategies
http://www.risk.net/type/journal/source/journal-of-investment-strategies

# LPPL model review
http://quantivity.wordpress.com/2011/02/08/curiosity-of-lppl/

# Tim Bollerslev
http://public.econ.duke.edu/~boller/Published_Papers/

# Doyne Farmer Prediction Company
http://www.oxfordmartin.ox.ac.uk/people/407
http://en.wikipedia.org/wiki/J._Doyne_Farmer



###########
### factor models anomalies investing smart beta

# Fama-French Factor Model
http://en.wikipedia.org/wiki/Fama%E2%80%93French_three-factor_model
http://www.calculatinginvestor.com/2011/04/19/fama-french-tutorial/
http://www.calculatinginvestor.com/octave-code/calculating-fama-french-loading/

# William J. Bernstein Three-Factor model step by step
http://www.efficientfrontier.com/ef/101/roll101.htm
http://www.efficientfrontier.com/ef/index.shtml

# Fama-French Factor Rolling Regression Viewer
http://mas.xtreemhost.com/

# Fama/French Forum
http://www.dimensional.com/famafrench/

# Current Factor Returns
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

# factor investing tutorial
http://www.finiki.org/wiki/Multifactor_investing_-_a_comprehensive_tutorial
http://www.bogleheads.org/wiki/Fama_and_French_three-factor_model

# Andrew Ang at Columbia book Asset Management: A Systematic Approach to Factor Investing
Ang Factor Models Investing.pdf
http://factorinvestingbook.com/
http://factorinvestingbook.com/book.html

# Tobias J. Moskowitz: Market Anomalies and Value and Momentum Everywhere
http://www.chicagobooth.edu/faculty/directory/m/tobias-j-moskowitz
http://faculty.chicagobooth.edu/tobias.moskowitz/
http://faculty.chicagobooth.edu/tobias.moskowitz/research/data.html

# Marcial Messmer and Dimtry Borisenko: didactic overview of CAPM and factor models - no code
http://www.factorstrategies.com/



###########
### sources and links

# Jason Strimpel: links to trading, quant finance, technology, statistics, big data, data science, mathematics
http://finance.jasonstrimpel.com/
http://links.jasonstrimpel.com/
https://github.com/strimp099

http://www.reddit.com/r/algotrading/

http://fishyoperations.com/

# hedge fund styles review
http://eranraviv.com/most-profitable-hedge-fund-style/



###########
### Frequentist Hypothesis Testing versus Bayesian Analysis

Charpentier Bayesian Analysis Frequentist Hypothesis Testing.pdf
Masson Bayesian Analysis Frequentist Hypothesis Testing.pdf

http://lesswrong.com/lw/g13/against_nhst/
http://statprob.com/encyclopedia/significancetestcontroversyandthebayesianalternative.html
http://www.bayesian-inference.com/hypotheses

# Bonferroni correction addresses the problem of multiplicity: rejecting a true null hypothesis (type I error) using a multiplicity of tests.
# as the number of hypotheses increases, the likelihood of witnessing a rare event increases, and rejecting a true null hypothesis (type I error).
http://en.wikipedia.org/wiki/Bonferroni_correction



#########
### prediction, forecasting, wisdom of crowds, human computation, superintelligence

# Good Judgment Project
http://www.goodjudgmentproject.com/
https://goodjudgmentproject.com/blog/
http://en.wikipedia.org/wiki/The_Good_Judgment_Project
http://www.washingtonpost.com/blogs/monkey-cage/wp/2013/11/26/good-judgment-in-forecasting-international-affairs-and-an-invitation-for-season-3/
http://www.npr.org/blogs/parallels/2014/04/02/297839429/-so-you-think-youre-smarter-than-a-cia-agent
http://technologie.gazeta.pl/internet/1,104530,15742315,Czy_tlum_jest_madrzejszy_od_najlepszych_analitykow.html
http://edge.org/conversation/how-to-win-at-forecasting

http://en.wikipedia.org/wiki/Clickworkers

# Pietro Michelucci at Human Computation Institute (HCI)
http://humancomputation.org/
https://www.sciencemag.org/content/351/6268/32.summary
http://www.technologyreview.com/view/538101/the-emerging-science-of-human-computation/
http://phys.org/news/2015-12-human-machine-superintelligence-world-dire-problems.html

# difference between prediction and forecasting
http://stats.stackexchange.com/questions/65287/difference-between-forecast-and-prediction
http://www.analyticbridge.com/forum/topics/difference-between-prediction
A prediction is a definitive and specific statement about when and what event will occur, for example: there will be an earthquake in Southern California this December.
A forecast is a probabilistic statement, usually over a longer time scale, for example: there is a 60% chance of an earthquake in Southern California over the next thirty years.
Forecasting can be considered a subset of prediction. 
In statistical regression analysis, predictions are in-sample model outputs, while forecasts are out-of-sample model outputs.
Nate Silver in The Signal and the Noise:
The term "predict" comes from Latin, while the term "forecast" comes from English Germanic.
In ancient times prediction was associatied with fatalism, fortune-telling, and superstition.
Forecasting reflects the new Protestant worldliness rather than the otherworldliness of the Holy Roman Empire. 
Making a forecast typically implied planning under conditions of uncertainty. It suggested having prudence, wisdom, and industriousness, more like the way we now use the word foresight.

# Galesic paper Wisdom of Small Crowds: small crowds may be better at forecasting than big ones
http://www.bloomberg.com/view/articles/2016-07-07/brexit-casts-doubt-on-the-wisdom-of-crowds

# Superforecaster failure at the Good Judgment project run by Philip E. Tetlock estimated likelihood of Brexit at only 23%
http://www.bloomberg.com/gadfly/articles/2016-06-24/trade-of-the-week-buying-nets-to-catch-helicopter-money
http://www.bloomberg.com/politics/articles/2016-06-24/they-got-it-wrong-swarms-of-global-chatterers-misread-brexit
http://www.bloomberg.com/news/articles/2016-05-18/superforecasters-see-24-chance-of-brexit-as-economy-wins-out

# British exit 2016: both prediction and financial markets failed to predict Brexit
http://www.bloomberg.com/gadfly/articles/2016-06-24/brexit-bookies-odds-and-the-hubris-of-efficient-markets
http://www.bloomberg.com/view/articles/2016-06-24/the-sun-not-the-rain-tipped-the-u-k-vote
http://www.optionsstrategynetwork.com/news/2016/6/25/brexit-provides-a-review-of-pitfalls-for-options-and-futures-traders
Just before the referendum vote on Thursday, the probability for the remain vote implied by betting odds reached 90% probability for remain.
Financial markets were also wrong, as they rallied into the vote. 

# Cameron Hight: forecasting ability feedback loop needed to improve forecasting accuracy
http://blog.alphatheory.com/2015/11/superforecasting-the-future-of-analyst-performance-measurement.html

# Alpha Theory accumulates forecasts from analysts, tests their accuracy and aggregates
https://www.alphatheory.com/home
http://blog.alphatheory.com/

# Brier score forecasting accuracy
https://en.wikipedia.org/wiki/Brier_score

# vetr crowdsourced star ratings for stocks
http://www.benzinga.com/general/crowdsourcing/15/01/5118917/vetr-a-platform-that-crowdsources-stock-ratings
https://www.vetr.com/

# crowdsource earnings forecasts
http://www.estimize.com/

http://www.predictwallstreet.com/

# stocks tweets from investors
http://stocktwits.com/

# wisdom of the inner crowd: reduce your own cognitive biases
https://hbr.org/2015/05/outsmart-your-own-biases
Make three estimates: high, low, and mid.
That makes middle estimate closer to expected value, than a two-number range would.
Make two forecasts and take the average. 
Premortem prospective hindsight: imagine a future failure and then explain the cause of it.
Take an "outside view" of project.

# BattleFin tournament for crowdsourcing best investment talent
http://www.battlefin.com/
http://www.battlefin.com/blog
http://dealbook.nytimes.com/2013/09/26/in-this-battle-arena-warriors-are-armed-with-algorithms/
http://www.businessweek.com/articles/2012-12-20/the-hedge-fund-hunger-games
https://www.kaggle.com/c/battlefin-s-big-data-combine-forecasting-challenge
# Mark A. Moore First Place in Battlefin Investment Contest
https://www.linkedin.com/pub/mark-a-moore-ph-d/1b/531/649

# Portfolios with Purpose paper trading contest
https://www.portfolioswithpurpose.org/
https://www.portfolioswithpurpose.org/how-it-works
https://www.portfolioswithpurpose.org/blog/

# Philip Tetlock book Superforecasting: Amateur forecasters are better than experts
http://blogs.wsj.com/moneybeat/2015/09/25/can-you-see-the-future-probably-better-than-professional-forecasters/
Philip Tetlock runs the Good Judgment Project.  
Amateur forecasters in the Good Judgment Project were about 30% more accurate than experts, even those with access to classified information.
Amateur forecasters were coached to be ruthlessly honest to themselves about their convictions, and why they hold them.
Amateur forecasters were coached to search for information that might disprove their convictions, and to change them at will, as often as new evidence emerged.
Amateur forecasters were coached to think in terms of probabilities in an uncertain world.
The best amateur forecasters were called superforecasters.
The superforecasters were more careful, curious, open-minded, persistent and self-critical (as measured by psychological tests) than others.
Superforecasters regard their views as hypotheses to be tested and adjusted, rather than certainties.
Experts are more confident and arrogant, and less willing to adjust their views.
Most experts are too quick to make up their minds and too slow to change them. 
Experts are also paid not just to be right, but to sound right, cocksure even when the evidence is sparse or ambiguous.
98% of economists expected interest rates to rise in 2014, but they fell instead.
Most energy analysts didn’t foresee oil price collapse in 2014.

# (10-28-15) Superforecasters predict no rate hike in 2015, not before January 2016
http://www.bloomberg.com/news/articles/2015-10-28/what-the-superforecasters-say-about-when-the-fed-will-lift-rates

# Philip Tetlock book The Fox vs the Hedgehog Forecaster
http://historysquared.com/2012/05/31/the-fox-vs-the-hedgehog-forecaster-by-philip-tetlock/
# Philip Tetlock: Expert Political Judgment
http://emilkirkegaard.dk/en/wp-content/uploads/Philip_E._Tetlock_Expert_Political_Judgment_HowBookos.org_.pdf

# Good Judgment Project by Philip Tetlock professor at Wharton
http://www.goodjudgmentproject.com/
https://goodjudgmentproject.com/blog/
http://en.wikipedia.org/wiki/The_Good_Judgment_Project
http://www.washingtonpost.com/blogs/monkey-cage/wp/2013/11/26/good-judgment-in-forecasting-international-affairs-and-an-invitation-for-season-3/
http://www.npr.org/blogs/parallels/2014/04/02/297839429/-so-you-think-youre-smarter-than-a-cia-agent
http://technologie.gazeta.pl/internet/1,104530,15742315,Czy_tlum_jest_madrzejszy_od_najlepszych_analitykow.html
http://edge.org/conversation/how-to-win-at-forecasting

# Good Judgment Project by Philip Tetlock professor at Wharton
https://www.gjopen.com/

# FutureMAP was a terror prediction market set up by U.S. 
http://www.bloombergview.com/articles/2015-11-20/preventing-terrorism-through-markets
Concern was that terrorists might profit from their own acts of terror, 
by making bets first and then committing acts of terror.

# British elections 2015: prediction markets failed to predict Tory victory
http://www.bloombergview.com/articles/2015-05-08/prediction-is-hard-especially-about-the-future-it-needs-improving-

# Prediction markets failed to predict no vote in Greek referendum 2015
http://www.bloombergview.com/articles/2015-07-07/the-wisdom-of-crowds-is-not-that-wise
http://trendfollowing.com/whitepaper/D4114-FatTailsNonlinearityLMIS.pdf
https://www.predictit.org/
Wisdom of crowds works well only when there's proper diversity, aggregation, 
and incentives.
Incentives can be part of a feedback loop.

# predictit prediction Wisdom of crowds
https://www.predictit.org/

# mindhive crowdsource evidence based policy
http://mindhive.org/

# augur crowdsourced forecasting using Ethereum by Joey Krug
https://www.augur.net/

# Nate Silver
http://fivethirtyeight.com/
http://fivethirtyeight.blogs.nytimes.com/2012/06/25/the-problems-with-forecasting-and-how-to-improve/
http://en.wikipedia.org/wiki/Nate_Silver

# Mike Bostock
http://bost.ocks.org/mike/
http://www.nytimes.com/newsgraphics/2014/senate-model/
# viewer of GitHub Gist code examples
http://bl.ocks.org/

# Dart Throwing Chimp
http://dartthrowingchimp.wordpress.com/



###########
### crowdsourcing, wisdomofquants, algorithmic trading platforms, risk systems

# Quantiacs trading platform with Python or Matlab IDE by Martin Froehler
https://www.quantiacs.com/

# Numerai San Francisco hedge fund founded by Richard Craib
https://numer.ai/
https://medium.com/@Numerai/encrypted-data-for-efficient-markets-fffbe9743ba8#.u30y2uofa
https://medium.com/@Numerai/rogue-machine-intelligence-and-a-new-kind-of-hedge-fund-7b208deec5f0#.rda4l7lx8
Numerai provides data to anonymous quants, quants submit stock price forecasts, and Numerai pays the best forecasters in bitcoin. 

# Numerai code in Python by Zygmunt Zajac
http://fastml.com/numerai-like-kaggle-but-with-a-clean-dataset-top-ten-in-the-money-and-recurring-payouts/
https://github.com/zygmuntz/numer.ai

# Technical Trading Robots based in Europe
http://predicio.com/

# sigmatrader trades random walk - is it a joke?
http://sigmatrader.com/

# Wootrader premium black box by Atanas Stoyanov
https://wootrader.com/
https://www.quandl.com/blog/wootrader
# Wootrader statistical engine for strategy allocation based on analysts estimates, fundamental analysis, technical analysis, options volatility
https://wootrader.com/how-does-wootrader-work/

# Automated Trading Robots for MetaTrader 5 MetaQuotes MQL5 language
http://www.mql5.com/
http://championship.mql5.com/
http://www.mql5.com/en/articles/584
http://www.mql5.com/en/articles/608

#  Java algorithmic trading platform, using Esper engine
http://www.algotrader.ch/
# Esper Open Source Complex Event Processing
http://esper.codehaus.org/
http://illposed.net/esperr.html

# Multi-asset, broker neutral automated trading platform
# Connect to any market destination or data feed, co-located infrastructure
http://www.quanticatrading.com/

# cryptotrader Bitcoin and cryptocurrency algorithmic trading platform
# provides backtest and algo automation using scripting language based on Coffeescript
https://cryptotrader.org/

# EquaMetrics RIZM algo trading with GUI (no coding)
http://equametrics.com/
https://rizm.equametrics.com
http://finance.fortune.cnn.com/2013/06/18/quant-trading-comes-to-main-street/

# PERL toolbox for building trading systems
http://www.geniustrader.org/
http://www.geniustrader.org/screenshots.html
http://www.geniustrader.org/examples/report.html

# JForex automated trading platform
http://www.dukascopy.com/wiki/

# Zorro trader scripting with API to IB
http://zorro-trader.com/

# Arthur Berd SmartQuant
http://www.smartquant.com/
arthur.berd@smartquant.com


###########
### credit scoring

# Joshua Kunst riskr package for credit scoring
http://jkunst.com/riskr/



###########
### Python Trading

# Mike Halls-Moore Python-centric Algorithmic Trading resource - very good
http://www.quantstart.com

# Algorithmic trading libraries written in Python
http://www.quantstart.com/questions/2/algorithmic-trading-libraries-written-in-python
http://quant.stackexchange.com/questions/8896/except-zipline-are-there-any-other-pythonic-algorithmic-trading-library-i-can-c

# PyAlgoTrade Algorithmic Trading Library with quantopian example
https://github.com/gbeced/pyalgotrade
http://gbeced.github.io/pyalgotrade/
http://gbeced.github.io/pyalgotrade/docs/v0.15/html/tutorial.html
# VWAP Momentum strategy quantopian example
http://gbeced.github.io/pyalgotrade/docs/v0.15/html/sample_vwap_momentum.html
# PyAlgoTrade group
https://groups.google.com/forum/#!forum/pyalgotrade
http://osrc.dfm.io/gbeced
http://www.freelancer.com/u/gbeced.html

# Python machine learning trading simple tutorial
https://www.toptal.com/machine-learning/s-p-500-automated-trading

# Python trading paid tutorial
http://www.tradingwithpython.com/
http://tradingwithpython.blogspot.com

http://nbviewer.ipython.org/urls/dl.dropbox.com/u/11352905/notebooks/trading%2520with%2520python%2520example.ipynb

# Thomas Wiecki probabilistic programming
http://twiecki.github.io/bayesian_risk_perf_boston_data_fest14.slides.html#/
# Thomas Wiecki Zipline in the Cloud
http://nbviewer.ipython.org/urls/raw.github.com/twiecki/zipline_in_the_cloud_talk/gh-pages/Zipline%2520in%2520the%2520Cloud%2520--%2520PyData%252013.ipynb

# Python Machine Learning for Trading course at Georgia Tech
http://wiki.quantsoftware.org/index.php?title=ML4Trading
http://wiki.quantsoftware.org/index.php?title=ML4TradingOverview
https://www.coursera.org/course/compinvesting1
# Python-based open source software framework designed to support portfolio construction and management
http://wiki.quantsoftware.org/index.php?title=QuantSoftware_ToolKit

# Tucker Balch Georgia Tech
http://www.cc.gatech.edu/~tucker/
http://augmentedtrader.wordpress.com/
https://lucenaresearch.com/

# ultra-finance data and trading strategies (Wes McKinney?)
https://code.google.com/p/ultra-finance/

# IPython Notebooks
http://nbviewer.ipython.org/

# RapidQuant paid service using RQlib: signal generation, backtesting, factors, risk analysis, portfolio construction, backtesting, and performance attribution
http://www.rapidquant.com/

# stock market structure in Python
http://scikit-learn.sourceforge.net/dev/auto_examples/applications/plot_stock_market.html



###########
### machine learning

# blog: Machine Learning Mastery
http://machinelearningmastery.com/
# Tour of Machine Learning Algorithms
http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

# Trevor Hastie
http://blog.revolutionanalytics.com/2013/05/hastie-glmnet.html
http://www.stanford.edu/~hastie/
https://www.stat.auckland.ac.nz/~yee/smartpred/index.shtml
https://www.stat.auckland.ac.nz/~yee/smartpred/DummiesGuide.txt
https://www.stat.auckland.ac.nz/~yee/smartpred/TechnicalGuide.txt
https://www.stat.auckland.ac.nz/showperson.php?firstname=Thomas&surname=Yee

# Mirko Krivanek's Blog (37)
http://www.datasciencecentral.com/profiles/blog/list?user=18dbktubfz01d

# Introduction to Statistical Learning: with Applications in R book by Gareth James, Daniela Witten, Trevor Hastie (Hardcover)
http://www-bcf.usc.edu/~gareth/ISL/
# Trevor Hastie web page
http://www.stanford.edu/~hastie/swData.htm
# presentations
http://web.stanford.edu/~hastie/TALKS/?C=M;O=D

# Michael Kearns UPenn machine learning trading and finance
http://www.cis.upenn.edu/~mkearns/

# Machine Learning tutorials by Andrew W. Moore
http://www.autonlab.org/tutorials

# Tom Mitchell (CMU) Machine Learning 1997
http://www.cs.cmu.edu/~tom/mlbook.html
http://www.cs.cmu.edu/~tom/mlbook-chapter-slides.html
http://www.cs.cmu.edu/~tom/10701_sp11/

# Machine Learning with Strata
http://strata.oreilly.com/ben
http://practicalquant.blogspot.com/
http://practicalquant.blogspot.com/2012/10/mining-time-series-with-trillions-of.html
http://practicalquant.blogspot.com/2013/10/deep-learning-oral-traditions.html

# Manifold learning is an approach to non-linear dimensionality reduction.
http://scikit-learn.sourceforge.net/dev/modules/manifold.html

# Machine Learning Taiwan lecture notes
http://www.csie.ntu.edu.tw/~htlin/course/ml13fall/doc/07_handout.pdf

# Machine Learning CalTech lecture notes
http://blaenkdenum.com/notes/machine-learning/

# Machine Learning from Streaming Data
http://blog.bigml.com/2013/03/12/machine-learning-from-streaming-data-two-problems-two-solutions-two-concerns-and-two-lessons/

# Support Vector Machines
http://www.reddit.com/r/algotrading/comments/14sipb/support_vector_machines/
http://www.reddit.com/r/algotrading/

# James Brofos Python Machine Learning
http://www.cs.dartmouth.edu/~james/
https://github.com/JamesBrofos


### SVM Support Vector Machines

# Huerta SVM Support Vector Machines Stock Selection
Huerta SVM Stock Selection.pdf
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1930709

# Alexander Smola - SVM and Kernel methods
http://alex.smola.org/
http://alex.smola.org/slides.html

# Linear classification Kernel trick
http://en.wikipedia.org/wiki/Kernel_trick


### CART

# CART (Classification And Regression Trees) Stock Market Forecasting
http://www.thertrader.com/2014/02/28/using-cart-for-stock-market-forecasting/
# Classification and regression trees
http://www.stat.wisc.edu/~loh/treeprogs/guide/wires11.pdf
http://www.analyticsvidhya.com/blog/2014/06/comparing-cart-random-forest-1/

# Axel Sunden thesis CART classification and regression trees trading
http://www.math.kth.se/matstat/seminarier/reports/M-exjobb10/100308b.pdf

# Loh CART Classification and Regression Trees
http://www.stat.wisc.edu/~loh/treeprogs/guide/wires11.pdf


### random forests

# random forest simple illustration
http://www.analyticsvidhya.com/blog/2014/06/introduction-random-forest-simplified/
http://www.analyticsvidhya.com/blog/2014/06/comparing-cart-random-forest-1/

# Random Forests in Python using the GPU
http://blog.explainmydata.com/2013/10/training-random-forests-in-python-using.html

# Python code Gilles Louppe dissertation Understanding Random Forests
https://github.com/glouppe/phd-thesis

# Alexander Didenko Finuniversity Moscow Random Forest Ensemble Learning Portfolio Views
http://moneymakingmankind.wordpress.com/
https://github.com/cocaineteddybear
http://quant.stackexchange.com/users/6535/alexander-didenko
http://moneymakingmankind.wordpress.com/2014/09/01/algotrading-in-bloomberg-r-and-beyond-financial-university-spring-fall-2014/



###########
### neural networks

# multiple linear regression using neural networks
http://www.stuartreid.co.za/misconceptions-about-neural-networks/
# Trading with Artificial Neural Nets
http://www.dukascopy.com/fxcomm/fx-article-contest/?Trading-With-Artificial-Neural-Nets&action=read&id=1300

# Matthew Dixon neural networks
http://stuart.iit.edu/faculty/matthew-dixon

# Nicholas Polson Bayesian models neural networks
http://faculty.chicagobooth.edu/nicholas.polson/
http://www.chicagobooth.edu/faculty/directory/p/nicholas-polson

# Kohonen Neural Network Self-Organizing Maps
https://en.wikipedia.org/wiki/Self-organizing_map
http://cortex.snowcron.com/index.html
http://cortex.snowcron.com/forex_som.htm



###########
### deep Learning and Boltzmann machines

http://www.economist.com/node/17305316

http://www.trade2win.com/boards/trading-software/105880-3rd-generation-nn-deep-learning-deep-belief-nets-restricted-boltzmann-machines.html

http://metacademy.org/roadmaps/rgrosse/deep_learning
http://www.wired.com/2013/12/facebook-yann-lecun-qa/

# Theano Python library
http://www.deeplearning.net/software/theano/
http://deeplearning.net/
http://deeplearning.net/2013/05/20/g-hinton-wants-to-train-neural-nets-with-trillions-of-parameters/

http://markus.com/deep-learning-101/
http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html

http://www.alexburns.net/tag/quantitative-trading/

http://quantlabs.net/blog/2011/07/quant-development-neural-networks-in-swing-trading/

http://censix.com/learning/

http://dekalogblog.blogspot.com/2013/02/restricted-boltzmann-machine.html

http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/

http://censix.com/tag/restricted-boltzmann-machine/

http://alandgraf.blogspot.com/?view=flipcard

http://deeplearning4j.org/restrictedboltzmannmachine.html

http://gigaom.com/2014/06/08/why-were-all-so-obsessed-with-deep-learning/

http://www.i-programmer.info/news/105-artificial-intelligence/7352-the-flaw-lurking-in-every-deep-neural-net.html
http://cs.nyu.edu/~zaremba/

# Ersatz Launches Industry First GPU Platform for Deep Learning
http://www.ersatzlabs.com/
http://www.programmableweb.com/news/ersatz-launches-industry-first-gpu-platform-deep-learning/2014/06/11
http://gigaom.com/2014/06/11/more-deep-learning-for-the-masses-courtesy-of-ersatz-labs/

# Skymind
http://www.skymind.io/
http://gigaom.com/2014/06/02/a-startup-called-skymind-launches-pushing-open-source-deep-learning/
# deep-learning library for Java, supported by Skymind
http://deeplearning4j.org/

# web Deep Learning
http://www.alchemyapi.com/

### Nvidia has developed the Tesla GPU specifically for deep learning
# CUDA Nvidia
http://www.nvidia.com/object/tesla-supercomputing-solutions.html
http://www.nvidia.com/object/tesla-servers.html

http://www.cio.com/article/750267/Nvidia_Pushes_Brain_like_Computing_with_New_Graphics_Products

Nvidia offers free trial on a remote cluster
http://www.nvidia.com/object/gpu-test-drive.html

Own a GPU Starter DevKit for $5,000 
http://blogs.nvidia.com/blog/2014/06/04/new-gpu-starter-devkit/

Yandex, Russia’s most popular search engine, is the first to use NVIDIA Tesla GPUs broadly in production to improve the quality of ranking web search results.
http://blogs.nvidia.com/blog/2014/03/25/machine-learning/

Nvidia has is partnering with AWS to offer their new GPU
http://www.nvidia.com/object/gpu-cloud-computing-services.html

# Getting Started with CUDA and Parallel Programming in the AWS Cloud
http://aws.amazon.com/articles/7249489223918169

# AWS offers cloud free for 12 months
http://aws.amazon.com/free/
https://aws.amazon.com/

# GPUs on AWS cloud for Deep Learning
http://techblog.netflix.com/2014/02/distributed-neural-networks-with-gpus.html



###########
### Finance Packages and Libraries

# QuantLib Library
http://quantlib.org/index.shtml
http://quantlib.org/reference/class_quant_lib_1_1_calendar.html



###########
### data sources

# best data sources
http://quant.stackexchange.com/questions/141/what-data-sources-are-available-online
http://quant.stackexchange.com/questions/18215/where-to-get-long-time-historical-intraday-data
https://tfetimes.com/best-quant-data-sources/
http://www.quandl.com/
http://blog.quandl.com/
http://quantpedia.com/Links/HistoricalData

# AQR
https://www.aqr.com/library/data-sets/
http://www.aqrindex.com/AQR_Momentum_Indices/Momentum_Research/Content/default.fs

# Wharton Research Data Services (WRDS) incl CRSP, COMPUSTAT, NYSE Trade and Quote
http://wrds-web.wharton.upenn.edu/wrds/
https://wrds-web.wharton.upenn.edu/wrds/about/WRDS%20FAQs.cfm
# WRDS tutorials
https://wrds-web.wharton.upenn.edu/wrds/E-Learning/
http://libguides.library.kent.edu/c.php?g=277981&p=1853494
# Using R with WRDS
https://wrds-web.wharton.upenn.edu/wrds/support/Accessing%20and%20Manipulating%20the%20Data/_007R%20Programming/_001Using%20R%20with%20WRDS.cfm
https://wrds-web.wharton.upenn.edu/wrds/support/Accessing%20and%20Manipulating%20the%20Data/_010Encoding%20your%20WRDS%20Password.cfm
# TAQMNGR processes raw data from WRDS (Wharton Research Data Service)
http://cran.r-project.org/web/packages/TAQMNGR/
# download WRDS data using package RPostgreSQL
https://iangow.wordpress.com/2011/08/01/wrds-sas-versus-postgresql/
# download WRDS data into R using SAS
https://iangow.wordpress.com/2012/09/03/remote-wrds-sas-from-r/
# download WRDS data using Perl script
https://github.com/iangow/wrds_pg
# Java program by bearcave:
http://www.bearcave.com/finance/wrds_data.html

# CRSP data
http://www.crsp.com/

# Federal Reserve Bank of St. Louis
http://research.stlouisfed.org/fred2
# GeoFRED map of FRED data
http://geofred.stlouisfed.org
# ALFRED to compare vintage data with the latest
http://alfred.stlouisfed.org

# Fama/French Factors
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

# data.table project
http://datatable.r-forge.r-project.org/

# OptionMetrics
http://www.optionmetrics.com/

### intraday and tick data from Yahoo and Google for U.S. stocks
http://www.quantshare.com/sa-426-6-ways-to-download-free-intraday-and-tick-data-for-the-us-stock-market
http://stat.researchforprofit.com/2014/01/15/intraday-prices/
http://www.marketcalls.in/database/google-realtime-intraday-backfill-data.html
http://stackoverflow.com/questions/15609334/how-to-download-intraday-stock-market-data-with-r

# AlgoSeek on Quandl
https://www.algoseek.com/

# Yahoo intraday and EOD data for U.S. stocks
http://theeclecticengineer.blogspot.com/2014/03/summary-of-yahoos-financial-api.html
http://www.quora.com/Is-http-chartapi-finance-yahoo-com-a-valid-web-service
https://developer.yahoo.com/forum/Y-OS-Documentation/Where-can-I-find-chartapi-finance-yahoo-com-documentation/1264748725000-703c455d-ad1e-30fd-880a-431518d40346/

# Garrett See
http://silkriver-edges.blogspot.com/2013/07/intraday-historical-data.html
http://silkriver-edges.blogspot.com/2013/07/r-for-trading.html

# data sources review
http://quant.stackexchange.com/questions/141/what-data-sources-are-available-online

# fundamental corporate data
http://stackoverflow.com/questions/10177435/get-annual-financial-data-for-a-stock-for-many-years-in-r
Factset
Compustat

# IBES Institutional Brokers Estimate System
http://financial.thomsonreuters.com/en/products/data-analytics/company-data/ibes-estimates.html

# vendors: CapitalIQ
# data source corporate Calcbench - 3k/yr
https://www.calcbench.com/

# CBOE
http://www.cboe.com/MktQuote/default.aspx

# American Association of Individual Investors Stock Investor Pro 
# fundamental corporate data earnings estimates cash flow, income statements and balance sheets
http://www.aaii.com/store/sipro
# backtesting using Stock Investor Pro
http://www.aaii.com/stock-investor-pro/article/Backtesting-Using-Stock-Investor-Pro
http://www.oldschoolvalue.com/blog/investment-tools/how-to-backtest-investing-strategies/

# Google public economic data + Data Explorer allows upload and visualization
http://www.google.com/publicdata/directory

# FINRA Corporate and Agency Bonds (TRACE), Municipal Bonds (MSRB) and end of day prices for U.S. Treasury Bonds.
http://finra-markets.morningstar.com/BondCenter/Default.jsp

# SEC EDGAR database of 10-K annual and 10-Q quarterly company filings
http://www.sec.gov/edgar.shtml#.VEzsi_ldXw8

# Nanex data
http://www.nanex.net/
http://www.bloomberg.com/news/2013-11-26/speed-traders-meet-nightmare-on-elm-street-with-nanex.html
http://physicsoffinance.blogspot.com/2013/06/nanex-visualizing-zillions-of-trades-in.html

http://www.quantshare.com/sa-43-10-ways-to-download-historical-stock-quotes-data-for-free

# Thinknum financial data and web platform for firm cashflow models, financial analysis and investment research
http://thinknum.com/
http://www.thinknum.com/browseFunctions
# Thinknum and R
http://www.econometricsbysimulation.com/2013/09/thinknum-new-interactive-public.html

# huge list of datasets and data repositories
https://dreamtolearn.com/doc/2HDNJH3XJU6CVGKZ7SDM4MCSW

# Market Data for 250/month
https://quantgo.com/

# Intraday historical data
http://r.789695.n4.nabble.com/intraday-historical-data-td4660996.html

# Institutional Historical Market Data
https://quantgo.com/?affid=17d

# well regarded sources:
http://www.iqfeed.net/index.cfm
https://www.cqgdatafactory.com/
# also eSignal TradeStation:

# public data aggregator
http://enigma.io/

http://www.eoddata.com
http://www.barchartmarketdata.com/
https://www.globalfinancialdata.com/
http://www.tickdata.com/
http://www.xignite.com/
http://www.ninjatrader.com/
http://www.kinetick.com/

# Datasets for Data Mining
http://www.kdnuggets.com/datasets/

http://www.google.com/finance
http://www.google.com/finance#stockscreener

http://www.tradingeconomics.com/

# ReturnFinder stock total return data
http://correctcharts.com/returnfinder/etfs

# Sirca data consortium
http://www.sirca.org.au/

# Mapping symbols between tickers, Reuters RICs and Bloomberg tickers
http://quant.stackexchange.com/questions/7568/mapping-symbols-between-tickers-reuters-rics-and-bloomberg-tickers

# stock codes mapping Reuters, Bloomberg, ISIN
http://www.tr8tool.com/

# R packages open data project
http://ropensci.org/
https://github.com/sckott/

# Reuters
http://thomsonreuters.com/financial/market-data/

# CME data
http://www.cmegroup.com/
http://www.cmegroup.com/trading/products/
http://www.cmegroup.com/product-codes-listing/month-codes.html
http://www.barchart.com/futures/specifications.php

load("E:/mktdata/ZQF3.rda")
E:/mktdata/ZQF3
load("E:/mktdata/sec/ESM9/2009.04.02.ESM9.rdata")
getSymbols("ES")

directories for RICS that I do recognize (like ESH3) are empty.
the directories with files in them have names that I can't recognize as RICS or anything related.
not in 1min bars
what is Price column

ZS	Soybean Futures
ZSK5	very large - 3,731,823 rows
ZSK5.N5	23,738 rows
ES*	empty dir
Euribor Futures empty dir
FEIBFZ4.Z5.Z6	empty dir
FEIH0	empty dir
german bund futures
GE*


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Quants & Systematic Trading
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