Deep dive analysis of risk metrics and indicators

(See below for most important links)

This is hopefully the last step before I go LIVE trading. This is the hardest and most involved step as described on my own blog. I will choose the best technologies and options I have presented. A lot of resources are my own as well.

**See bottom for both most sensible to use as well as most convenient**

*Deep dive analysis of risk metrics and indicators *

*Available from my free Quantlabst.net/blog by searching ‘portfolio optimz’*

*Portfolio optimization:*

# Markowitz portfolio optimizaion and Bayesian Regression

# Optimize Portfolio with CVaR in Matlab

# Entropy and Optimization of Portfolio for quant analysis

https://quantlabs.net/blog/2012/02/entropy-and-optimization-of-portfolio-for-quant-analysis/

# The Efficient Frontier: Markowitz portfolio optimization in Python

https://blog.quantopian.com/markowitz-portfolio-optimization-2/

A Novel Algorithmic Trading Framework

Applying Evolution and Machine Learning

for Portfolio Optimization

http://blog.andersen.im/wp-content/uploads/2012/12/ANovelAlgorithmicTradingFramework.pdf

PortfolioSelectionwithRobustEstimation

http://www.est.uc3m.es/fjnm/esp/papers/RobustPortfolios.pdf

# Advances in Cointegration and Subset Correlation Hedging Methods

http://ssrn.com/abstract=1906489

# High return with low risk?

Tail hedge risk

https://quantlabs.net/blog/2016/08/high-return-with-low-risk/

Optimal F

https://quantlabs.net/blog/2012/03/position-sizing-for-quant-analytics/

*** Simplified Risk management http://www.seykota.com/tribe/risk/index.htm

# Quant analytics: Paper for the Black Litterman Model: BLACK, F. (1989): “Universal Hedging:

# Factors on Demand: Building a Platform for Portfolio Managers, Risk Managers and Traders

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1565134

# Why expected return factor models and risk factor models are different? Why expected return models and risk models use different factors?

This issue is addressed in an MSCI Barra research paper that’s available online:http://www.msci.com/resources/research/articles/2008/RI_Do_Risk_Models_Eat_Alphas_April_08.pdf.

There is also a JPM paper authored by three people from Goldman Sachs that emphasizes the need for including alpha factors in a risk model:http://www.iijournals.com/doi/abs/10.3905/jpm.2007.674791.

COT reveals something about existing traders’ positions hence big money sentiment. Yes I’m not surprised there’s a link to that and subsequent trends. Also Call/put ratio, VIX, Bullish Percent index, Trader sentiment surveys (American investors) show a little about sentiment hence are worth investigating as leading indicators.

…

If you believe a bit in Markowitz and you believe that there are no correlations its indeed a good idea to do the 2:1 split as in your example. The expected profit for your position p (say number of shares or contracts) in an asset is

PROFIT = p * volatility * expected Sharpe

I think of risk position r = p * volatility.

Markowitz in its most simple form tells you therefore r ~ inv(Correlation Matrix) * expected Sharpe. If your assets are uncorrelated… eh, voila.

Of course you can pimp your utility function and include penalties for updating positions, etc. I am a big fan of sparse portfolio updates using the L1-Norm (papers by Daubechies…).

https://quantlabs.net/blog/2011/08/quant-analytics-algorithmic-trend-detection-methods/

*Search on blog for ‘position siz’ meaning position sizing*

# Day trading money management: Position sizing

https://quantlabs.net/blog/2016/09/day-trading-money-management-position-sizing/

# What leverage do you use and why for forex?

What leverage do you use and why for forex?

Understanding Leverage and Margin in Forex Trading and Avoiding… pipburner.comForex leverage and margin explained with easy words. Find out example on how to prevent losses and use forex leverage efficiently for your trading.

–I think leverage is the consequence of the strategy one chooses, and its risk. If one trades with a standard 2 or 3% risk per trade, that provides the position size (and leverage), which will normally be way below 400:1 or even 50:1!

–So you think, leverage should be less than 50:1?

==Normally, yes. For example, let’s imagine you have an account with a margin of USD 10,000, and you want to buy EUR/USD at 1.3050 with a stop loss at 1.295 and profit at 1.325. The risk per trade would be 100 pips, or 100 USD per mini lot (10,000 units). With a strategy of 2% risk per trade (i.e. 200 USD), that would allow you to buy 2 mini lots (20,000 EUR/USD), so the leverage would be 2:1. In the same situation, a leverage of 20:1 (buying 2 standard lots) would mean to risk only 10 pips per trade.

So the strategy defines the leverage.

I read your comment that a leverage of 100:1 might work. The only case I can see that working in the long run is with very tight stop losses. Were you talking about a particular strategy? Thanks3 days ago• Like1 Follow CasemCasem Tong • you see, when you lower down the risk per trade e.g 4% to 2 or 3% or lower the leveraging is always go for minimal as you can ,those 50:1 or 100,200 :1 is consider gambling ” win or lose ” Manuel said 20:1 ( buying 2std lots) risk only 10pips but do you consider if the directions go against you then the 10pips is meaningless….

https://quantlabs.net/blog/2012/04/what-leverage-do-you-use-and-why-for-forex/

# Quant development: Who is using R to develop and test trading algorithms?

Have a look at quantstrat, blotter, FinancialInstrument and PerformanceAnalytics…

Also RBloomberg and Ibrokers DEoptim for optimisation.

‘*Money management’ search *

# Money Management for Trading Advise video

https://quantlabs.net/blog/2014/06/money-management-for-trading-advise-video/

# Kelly Criterion for basic risk management and self adapting money management

# 1 5 hour Meetup replay of Risk parameters and money management in a self adapting automated trading

Questions found here: https://quantlabs.net/blog/2014/11/questions-for-risk-parameters-and-money-management-in-a-self-adapting-automated-trading-world/

….For example, a 100K with 25K deposited and 5% return. After exposure goes up 4x, 100K exposure on 8-10 positions with 25% cushion. If margin goes up to 30k, you can expose at 5x with total 150k on 10-12 positions. You need to increase your risk and diversity on portfolio. You increase your exposure as you improve to be more profitable. Your risk goes down as your portfolio becomes more diverse with a few more positions on each iteration. You need set up an upper limit with a upper limit of the exposure to apply. Your volatility of the portfolio will go up in the long run. In a pure long/short S&P 500 portfolio, never go beyond 6x exposure. A Standard Deviation 2 could knock you back to the start. If you add forex, the mix of the portfolio changes thing. After 2nd iteration, you want to ensure you add the right mix of forex, equity, and commodity so each month you get a nice return to rapidly build you portfolio. You could double your money after 1 year if you keep your exposure 5x with 25% annual return. You should add another 25K in 2nd second with another 50k added in third year. This can only be done when you when cut your losers fast and run the winners. with discipline in mind. Retail traders will pretty well break even at best.

# Having discipline in your risk management for long term trading for profit

(pro way!)

# Why vast amounts of retail traders lose money? Applying the day trading mode when volatility is low to make money

(pro way)

# How to apply psychology of trading when you put your positions on? CRITICAL reason to automate while retail traders WILL always lose money

(pro way)

# Quant opinion: The three myths of modern risk management part 1

Volatility and VaR are stictly “backward looking”, so they work as long as the past is what is going to happen in the future. But during bubbles this is a naive assumption. Note that volatility and VaR are typically low during periods of over-optimism just before a bubble bursts. Totally useless.

The one thing to remember is that even market aggregates can become overvalued, so the G-D value principle applies to the market in aggregate as well as for individual companies, a fact missed by some value investors.

I have written a white paper on the subject here (note also my comments on risk and volatility):

http://www.pionline.com/assets/docs/CO72842217.PDF

https://quantlabs.net/blog/2011/05/quant-opinion-the-three-myths-of-modern-risk-management-part-1/

Which affects your currency trading?

Demand for storage and foreign currency trading

You can do currency trading in the futures market. The position size are manageable for individuals. You don’t need a big movement for profit. Leverage can be used to increase your profit. Interest rates affect currency markets who pays the highest. Inflation will affect the purchasing power of a particular country. Monetary policy affects by tightening or loosening money supply. Trade balances affect importing or exporting of the country. Economic growth affected based on Business cycle of a country as in recession or recovery. Political stability where there could be a political change.

https://quantlabs.net/blog/2015/07/demand-for-storage-and-foreign-currency-trading/

# Is there any Relation between spot currency market (forex) and option currency market

I think the simplest implementation would be to trade FXE options which are listed on the major US option exchanges. FXE is the euro ETF which trades over 1,400,000 shares a day. There is enough open interest to allow you to pursue some simple strategies. As long as your account is set up for listed option trading, you can trade FXE

Is there any Relation between spot currency market (forex) and option currency market

**MOST RELEVANT LINKS From ABOVE in Python **

http://blog.andersen.im/wp-content/uploads/2012/12/ANovelAlgorithmicTradingFramework.pdf <– great research for machine learning with Portfolio Analytics/Risk/ etc

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1906489

Basic definitions: http://www.seykota.com/tribe/risk/index.htm

https://blog.quantopian.com/markowitz-portfolio-optimization-2/

These might be the most convenient and easiest for Python

http://work.ange.le.free.fr/works/MarkowitzPortfolio/MarkowitzPortfolio.pdf <— this is very comprehensive and exactly what I want

https://www.quantopian.com/posts/the-efficient-frontier-markowitz-portfolio-optimization-in-python-using-cvxopt

https://blog.quantopian.com/markowitz-portfolio-optimization-2/

http://dx-analytics.com/11_dx_mean_variance_portfolio.html

http://travisvaught.blogspot.ca/2011/09/modern-portfolio-theory-python.html

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*TRADING ALERTS*