Tag Archives: summary

NEW Summary analysis report for 13 long short crypto currency for June 8

NEW Summary analysis report for 13 long short crypto currency for June 8

I have included 2 samples of my latest crypto currency from Binance. This has scanned 315 pairs which generates 7 shorts and 6 longs. Most of these signal are false but this is part of the dilemma you will have if you do not combine the right indicators before taking the trade.

Video benefits attached

This 34 minute showcases all the issues and concerns you can have regardless of what of asset class you focus on. Crypto is very volatile so It looks like I have put on emphasis on volatility of the individual crypto pair versus a group volatility of the crypto that generate a buy or sell signal. I am still gauging volume as well if it is worth taking a trade or not.

Here are the sample files that will be part of Quant Analytics service

Sample files as explained in the video

CryptoLong

CryptoRank

CryptoShort

Analyzing 3700+ charts for crypto currency algo trading opportunity include ETH LTC NEO

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Money management basics and summary videos

Money management basics and summary videos

These came in and could be worthwhile videos to watch

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Summary on hedging strategies for buying commodities for future and options trading

Summary on hedging strategies for buying commodities for future and options trading

Summary , hedging strategies, buying, commodities , future ,options, trading

Get ACCESS to this C++ source code here or get immediate access here 

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Nassim Taleb Bloomberg Quant Finance summary and notes

Nassim Taleb Bloomberg Quant Finance summary and notes

May 29th 2015 Bloomberg Quant Finance Seminar:
Nassim Taleb Presented ‘The Law of Large Numbers in the Real World’
Takeaways:
– Fat Tails exist can be described by some negative exponent Alpha
– The Tail or Alpha distribution is Normal even though the Price
and Return distributions are not ( Use Pareto or Power Law Distribution for them, 80/20 Rule)
– Minimum sample space needed for convergence to a stable moment: Normal – 30, Pareto – 10^14 (100 Trillion)
– Asked Nassim about what type of historical financial data he used
He responded that I should take a look at his paper. But I suspect it is mostly monte carlo generated data. I didn’t find any specific data sources yet. But he claimed to be able to capture the fat tail
behavior of the SPX, by finding a stable moment in the power law
distribution, K*Alpha* X^ -Alpha
– Julien Guyon (Author: NonLinear Option Pricing) didn’t think there
was any info on how to price options in Nassim’s Lecture.
– Any distribution using a financial time series sample space
would be finite and thin-tailed with compact support in time and price.

Thanks to the NYC Contact for sending this

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Here is a summary of message queueing libaries but which one? Kafka? ZeroMQ?

Here is a summary of message queueing libaries but which one? Kafka? ZeroMQ?

I just learned about Kafka which shows lots of promise. I am only experienced with ZeroMQ 
but it  did not have the failover. Let me know which one you would go with?
http://queues.io/
Another good comparison  http://www.bravenewgeek.com/tag/kafka/

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Was this is the best Bloomberg Quant Seminar? Here was a summary

Was this is the best Bloomberg Quant Seminar? Here was a summary

Thanks to Sholom for sending

Professor Jim Gatheral Speaks about Fractional Volatility Models

Summary:

– Use Fractional Brownian Motion to model SP500 Realized Variance

– The different of the logs of realized variance are gaussian

-The Hurst number is used as a degree freedom.
Just like a Student t distribution describes SP500 returns over time
by varying the degrees of freedom n (n=100 for low volatility environments and n=1 for very high volatility environments)

-For the really spiky parts of the realized variance distribution, the Hurst Number is set to .14 giving a very jumpy infinitely discontinuous function that is still measurable so you can use Lebesgue Integration to compute the sum over the high volatility time interval of Fall 2008.

– The open question is how to predict what hurst number to use at what time interval which would make the model predictive/forward looking instead of just descriptive.

Nassim Taleb started a huge fight with Jim claiming that the model
makes no sense since realized variance is not gaussian even when you compute the difference of the logs. Jim responded with the change in hurst numbers from 0.5 (random walk) to 0.14 (extreme discontinuous jumps). Nassim then made a comment about Rational Expectations which Jim agreed with. I think Nassim was
saying that you can’t predict huge jumps, so you remove them from
your predictive model and only include the extreme jumps as part of your descriptive model.

Extreme Modeling vs Rational Expectations:

Nassim Taleb seems to always think in terms of extreme events
(i.e. black swans induced by exogenous information). Jim Gatheral
took a more Rational approach by saying omit the extreme events since they are rare and you can’t predict them. Once you protect
yourself from black swan events, then use a model that predicts
all other types of events (driven by endogenous information)
to form the basis of your trading strategy
Best Bloomberg Lecture so far!!

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Here is the IAQF Trading Historical Backtesting Talk Summary for a quant model and strategy

Here is the IAQF Trading Historical Backtesting Talk Summary for a quant model and strategy

 

– Hold-Out Data Testing is insufficient

– 100% Overfitting is where your model will model only the sample space instead of the thing you are trying to model.

– Overfitting is a % of bias in your test data and should be computed as a probability.

– Probability of Backtest Overfitting (PBO) is used to determine how much capital to allocate to each strategy

– Maximum PBO that should be  allowed for trading is .20

according to Marcos.

– Overfitting is unlikely with very simple strategies or with complex strategies that use a lot of of data (i.e tick data for HFT) but few variables.

-Neural Nets with 20 or more variables are highly susceptible to overfitting.

– Non-linear estimators like polynomials are more susceptible to noise than linear estimators like OLS

– Symmetric combinations of out of sample test data should

be used for testing (i.e multiple hold-outs)

– Transformations can be used to remove the bias in non-normal distributions and in linear estimators (OLS)

-Neural Nets/Machine Learning can be used to find the optimal transformation needed to remove the bias from the sample space (This is known as Deep Learning)

 

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2326253

 

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2308659

This came from my NYC contact so big thanks to him for sending this

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Webinar with R trading with complete workflow to build strategy, plot, P&L, rules, indicator, signal, portfolio summary ,trade signal stats

Webinar with R  trading including complete workflow to build strategy, plot, P&L, rules, indicator, signal, portfolio summary ,compute trade/signal statistics

This demo coming soon will contain the following steps:

  1. create the porfolio and account
  2. Plot something like simple moving average
  3. Create your Buy-Sell rules from creating potential short/long positions to exit position
  4. Calculate P&L and resulting equity
  5. Plot the performance
  6. Initialize your set of orders
  7. Crate a new strategy
  8. Add a potential indicator to the strategy
  9. Add a rule to the strategy
  10. Add a signal to the strategy
  11. Execute the strategy
  12. Update the P&L
  13. Plot the portfolio summary time series
  14. Compute return and trade statistics

 

 

Be on the lookout by signing up here for this strategy as we will announce the demo date to the public. It will be very limited as it will only be demonstrated once. After that, it will only be available to our Premium Members.

 

UPDATE: There will be 3 separate webinars for each part to be demoed.

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Summary of links for matlab forex trading, matlab forex prediction, matlab forex indicator, matlab forex signal processing

Summary of links for matlab forex trading, matlab forex prediction, matlab forex indicator, matlab forex signal processing

Here are a summary of interesting links for keywords:

**Keyphrase: matlab forex trading**

trading strategy back tester

http://www.mathworks.com/matlabcentral/fileexchange/30693-trading-strategy-back-tester

IR / FX / Currency Derivatives Matlab code

http://www.global-derivatives.com/index.php?option=com_content&task=view&id=184

Deploying Advanced Mathematical Strategies for Automated FOREX Trading (INSTITUTIONAL)

http://www.currency-investor.net/article/8/deploying-advanced-mathematical-strategies-for-automated-forex-trading

MATLAB applications of trading rules and GARCH with wavelets analysis

http://www.currency-investor.net/article/8/deploying-advanced-mathematical-strategies-for-automated-forex-trading

Market forecasting: the predictive power of price patterns
(Price patterns frequently associated with the continuation or reversal of trends are recognizable.)
http://www.advancedsourcecode.com/stockpredictionpricepatterns.asp

**matlab forex prediction **
http://madis1.iss.ac.cn/madis.files/pub-papers/lncs-04-03.pdf

**matlab forex indicator
mostly links about Metatrader 4 and Matlab DLL link

**matlab forex signal processing

Nothing of importance

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Here is a Java summary for your job interview question

Here is a Java summary for your job interview questions
Constructors need to start with this or super() calls.
Call static methods by className.staticMethod() where staticMethod() is static in classname class.
Button b=new Button () where b is new reference variable of a Button object. Created on heap while local variables live on stack.
Pass by reference where
Dimension d= new Dimension(); modify(d); will modify object variables but reference variables like
int x; modify(x) will not change x since it is a a reference variable.
Use auto boxing for methods like Float f=new Float.valueof(“1.221f”), pasrseXXX, or
String s=”hex=”+Long.toString(123.12);
Regex include \d,\w (word), \s, + (one or more), * (zero or more), ? (zero or one)
To tokenize use String.split(s) where s is a String.

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