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1  Economy / Exchanges / Re: Official Quoine Exchange Thread on: December 13, 2016, 05:22:23 AM
Why is the exchange off line, update please
2  Alternate cryptocurrencies / Announcements (Altcoins) / Re: [OFFICIAL] [SEC] Safe Exchange Coin [website www.safex.io] on: March 04, 2016, 04:55:33 PM
reposting in the new thread

Isn't this what Augur is doing ? http://www.augur.net/

What advantages does SEC have over Augur ?

Augur is a huge project, currently I see the them being massive competition.

Thoughts and opinions on this, please
3  Alternate cryptocurrencies / Announcements (Altcoins) / Re: [ANN] Official, Safe Exchange Coin [SEC] - safex.io/ on: March 04, 2016, 06:19:38 AM
Isn't this what Augur is doing ? http://www.augur.net/

What advantages does SEC have over Augur ?

Augur is a huge project, currently I see the them being massive competition.

Thoughts and opinions on this, please
4  Bitcoin / Project Development / Re: [ANN] FACTOM - Introducing Honesty to Record-Keeping on: February 16, 2016, 07:49:27 PM
Yeah, sorry I know that. I was asking if it was legit, are factom officially affiliated with them ?
5  Bitcoin / Project Development / Re: [ANN] FACTOM - Introducing Honesty to Record-Keeping on: February 16, 2016, 06:24:47 PM
Is this article from China legit ? https://justpaste.it/rfom
6  Alternate cryptocurrencies / Altcoin Discussion / Re: Custom indicators for use in predictive models on: February 02, 2016, 07:24:29 PM
I see what you did with the Ethereum Grin

Dump prediction indeed seems problematic.

The reduced profit in coins like DASH and ripple, NBT could imply that the coin itself has fundamental issues, like premine or the coin rules could be less fair (weak decentralization, other issues).


However the thing that surprised me was Litecoin.

I believe NBT is a 'stable coin', the value of 1 NBT is pegged to 1 USD. If we could predict the value for this we could predict bitcoin, which unfortunately our models can't do, it appears to be too efficient for our models to to find any inefficiencies. This goes for DASH, LTC and ETH, they appear to be to efficient for our models
7  Alternate cryptocurrencies / Altcoin Discussion / Re: Custom indicators for use in predictive models on: February 02, 2016, 07:16:08 PM
I don't believe that TA will work for crypto markets when the value is so small. A little bit of btc can manipulate the market up or down so TA is useless here. Unlike stocks where the market cap is big, TA works much better there.

Our back test results tell a different story.
8  Economy / Speculation / Custom indicators for use in predictive models on: February 02, 2016, 09:23:32 AM
I work with a couple of AI / machine learning PhDs, we're currently adapting their models to the crypto markets. These markets seem to be young and fairly inefficient, one model we're focusing on is producing very interesting backtesting results. Here are the results for anyone that's interested: https://www.mediafire.com/?57qzdvn71rqaacl

The model in question is based on recurrent neural networks and hawkes processes, the premise of it is to take TA indicators then use machine learning to auto weigh the importance of these indicators to accurately predict the direction of the price into the future. It continuously learns over time and updates its parameters given changing market condition

Currently we're using TA-lib, a python library of 150 TA indicators as the models inputs. It seems to be quite good at investing at early stages of pumps,  however it's poor at predicting price reversals ie knowing when sell. Obviously this isn't something trivial, events such as dumps can be completely random.

We want to improve the models performance and optimize it to act more efficiently in these markets. One way to do this is to use custom indicators that have been designed to work with specifically with these markets.

One idea I had was to use loan rates on poloniex. It seems these loan rates are much higher for coins before they dump, although they are high at other times as well, by making an indicator from:
a. the LoanOffer rates
b. the amount of active loans

we could feed this into the model to determine if it can find any patterns between how these metrics change with what the price does.

Has anyone had any experience with this ? I'm trying to figure out how the indicator would be programmed. Currently i'm thinking making an oscillator going from the minimum interest rate to the max ( 5% / day on polo). The rate of loans being taken and the amount of loans currently out for loan could also be used in another indicator somehow

This is just one idea I had, if anyone has written other custom indicators they use in these markets and is interested in what we're doing, please feel free to drop me a pm.

Thanks for your time
dave@sheffieldcrypto.com
9  Alternate cryptocurrencies / Altcoin Discussion / Custom indicators for use in predictive models on: February 02, 2016, 06:17:20 AM
I work with a couple of AI / machine learning PhDs, we're currently adapting their models to the crypto markets. These markets seem to be young and fairly inefficient, one model we're focusing on is producing very interesting backtesting results.For anyone that's interested: https://www.mediafire.com/?57qzdvn71rqaacl

The model in question is based on recurrent neural networks and hawkes processes, the premise of it is to take TA indicators then use ML model to auto weight the importance of these indicators to accurately predict the direction of the price into the future. It continuously learns over time and updates its parameters given changing market condition


Currently we're using TA-lib, a python library of 150 TA indicators as the models inputs. Currently it seems to be quite good at investing at early stages of pumps,  however it's very poor at predicting price reversals ie knowing when sell. Obviously this isn't something trivial, events such as dumps can be completely random.

We want to improve the models performance and optimize it to act more efficiently in these markets. One way to do this is to use custom indicators that have been designed to work with specifically with these markets.

Which get's me to the topic of this post. It seems the loan rates on poloniex are much higher for coins before they dump, although they are high at other times as well, by making an indicator from:
a. the LoanOffer rates
b. the amount of active loans

We could feed this into the model to determine if it can find any patterns between how these metrics change with what the price does.

Has anyone had any experience with this ? I'm trying to figure out how the indicator would be programmed. Currently i'm thinking making an oscillator going from the minimum interest rate to the max ( 5% / day on polo). The rate of loans being taken and the amount of loans currently out for loan could also be used in another indicator somehow

This is just one idea I had, if anyone has written other custom indicators they use in these markets and are interested in what we're doing, please feel free to drop me a pm.

Thanks for your time
dave@sheffieldcrypto.com
10  Bitcoin / Project Development / Trading systems needed - machine learning forecasting start-up on: December 29, 2015, 02:37:19 PM
I work with two PhD’s that develop machine learning models to forecast financial markets, through out this year we have been experimenting with applying them to bitcoin and other digital currency markets. One model we have developed seems find various inefficiencies seen in the markets that govern ‘alt coins’.

Here is a link to the model running on an AWS. http://ec2-52-27-84-167.us-west-2.compute.amazonaws.com:8000/webctrade/?modelname=TATraderMulti&coinname=DASH

The model is based on Hawkes processes and recurrent neural networks. It is currently set up on a 24hour time frame and makes a new prediction at 00:00 UTC every night. One innovation was not predicting price directly but to optimize for trading profit directly. The signal seen represents the percentage of your portfolio  (each coin trades it’s own portfolio) you should invest / the confidence and polarity of a price move on the next time step

Please view the backtest we ran across multiple coins at the end of 2014: http://sheffieldcrypto.com/wp-content/uploads/2015/06/backtest-1.txt

Scrolling to the bottom, one can observe the ‘pnl portfolio_value’. Starting with $5,401 the trader finished with $25,671 after a 3 months period, this is taking into consideration exchange fees however the trader assumes it could make a trade on the closing price, given the liquidity of some of these markets this may have not been possible, this requires some kind of strategy that optimizes for intra day trading during periods of high volume.

At times during this backtest you can see the trader made much more than the final profit however it was not able to anticipate the flash crashes seen on blackcoin.

Besides this model we are also developing a deep learning model and NLP (natural language processing) model, this will utilize various social media as its inputs.

What we require now is someone with experience in trading systems who can help us implement our model into a trading scenario (the model is currently configured to poloniex and written in python). www.genesismining.com have shown interest in partnering with us, they have an existing system that connects to many exchanges, the API to connect to their infrastructure can be seen here http://pastebin.com/zM8A3MeG. We would also be open to connecting to poloniex directly.

If you have any questions, please contact us directly at 
dave@sheffieldcrypto.com
 
Regards,
Dave
www.sheffieldcrypto.com
11  Bitcoin / Bitcoin Discussion / Re: Looking for someone with experience with automated trading and market analytics on: August 18, 2015, 06:37:17 AM
You know you're absolutely right James. There's not Sheffield media on our page at all, i'l add some later, thanks for bringing this to my attention.
12  Bitcoin / Bitcoin Discussion / Looking for someone with experience with automated trading and market analytics on: August 10, 2015, 08:58:15 AM
Dear Reader,

My name is Dave, I am a partner at SheffieldCrypto, we specialise in developing cutting edge predictive models. The models utilize machine learning to forecast various movements, in our case we have applied them the markets which govern cryptocurrencies. Please see our website for more infomation http://sheffieldcrypto.com/.

On one simulation we tested one of our algorithms with around 30 coins with shared initial parameters over a 3 month period, however many of these coins had little data (small market caps), the results were as followed:

Start capital: $5,000
TA trader: $12,706
Buy and Hold: $-731.79
Random $-1,145
Buy and hold and random are included as comparisons ie how your portfolio would have performed if you were to 'buy and hold' the commodity

Another model being developed is based on NLP (natural language processing). This relies on input from social media or ‘text data’, such bitcointalk threads, reddit, twitter etc. The semantical content is analysed and the algo finds patterns between that and price movements. This is similar to the TA algo where the system finds patterns in price data and attempts to correlate it to future price movements. We are currently working on combining the output from both algos into a deep neural network. The algorithms are adaptive, they are constantly learning and changing their parameters given new market conditions. These techniques are currently being used by large hedge funds and other financial institutions. The techniques we use are similar to the ones seen in this article.

We have 3 PhD’s on our team developing these models. Time is limited and we require someone with experience in:
  • Market analysis
  • Automated trading systems/algorithmic trading
  • Custom indicator design

to help us design a trading system in which these predictions can be used. Currently the TA algo utilises the output from TA-lib (a library of 150 technical indicators). The design of custom input parameters (or features as they’re known in machine learning) will allow the models to make better predictions by feeding it more relevant market data. However the priority at the moment is the design of a trading system. As short selling is now available for some cryptocurrencies, the prospect of experimenting with this when the models give negative signals is also of interest to us (currently we have only considered positive signals to go long).

The models collectively have had 6 years of development (3 years for each PhD) and we are now ready to implement them into a trading scenario.

If you feel you or a colleague of yours would be suitable for our requirements please reply or pass this message on.

Yours sincerly
Dave - marketing and networking
dave@sheffieldcrypto.com
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