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Shannon demon forex converter

Автор:Tosida Category: Ethereum gear 2 Окт 12

shannon demon forex converter

the probability measure for random states and Shannon's entropy measure as the These implement maxmin-minimax principle of converting process entropy to. Becky McKay and Shannon Mante have been invaluable It's hard for people to confront their demons, but if you do so, you'll be richly. plan has a goal of cutting cancer death rates by at least 50 percent over the for survivors of domestic violence, as well as funding to support a demon-. BREAK EVEN POINT BITCOIN MINING

However, I was quickly sold. I had no particular perspective on whether a sequence of numbers in a shared ledger somehow represented the future of finance and currency. We fired a few emails back and forth to share blog posts detailing all of the ridiculous things that were happening at that point Fall in the crypto markets and laid plans for our first attempt at riches.

Interexchange arbitrage In October of , dabblers in cryptocurrency were faced with a curious fact: the price of an individual asset varied across exchanges. Those well-versed in finance will read this sentence in disbelief.

There is no sane reason why an asset that costs nothing to transport should be selling for different prices in different markets. However, the crypto exchanges were not sane. Risk-free profits were available to any trader who can buy on GDAX and sell on Bitfinex when the prices converged and vise-versa when they diverged. After looking at the figure above for a minute, an obvious trading strategy emerges.

If we bought BTC with USD on GDAX at the minimum gap and sold at the maximum gap and did the reverse on Bitfinex , we would make around a hundred bucks a trade and dozens of such trades were available every day! We are leaving out some details regarding execution, BTC long exposure, and interexchange transfers, but a merely clever person with a free AWS instance can work this out. This is in sharp contrast to the established markets where profiting from pure arbitrage requires several PhDs in physics and access to millions of dollars in hardware.

In theory, we enter the one leg of the trade when the blue line rises above the orange line and reverse the trade when is falls below the green line. We worked through the exact strategy and set it in code. It backtested beautifully. Free money was materializing before our very eyes. Soon, we would throw off the shackles of labor forever! A week later, we were ready to go. Wary of holding too large of a long BTC position or too much cash on any of the exchanges, we also included a recycle leg that involved transferring both between exchanges.

This was a mistake. Nearly immediately after hitting go, transactions froze. Early December was the peak of crypto mania. As soon as the block was confirmed, we would be ready to go again and keep that arbitrage machine humming. But we waited. And waited. Our trading machine ground to a halt. The BTC network had become so congested that simple BTC transactions were taking days or weeks and only those with mining rigs could push them through.

As the days passed, renting a miner to unstick our transaction became a real option, but finally by some good graces the BTC appears in our GDAX account. We had made exactly two trades in nearly a week. Sloshing dollars and Bitcoins between exchanges and accounts was clearly not going to work.

We ran a few experiments by hand to accelerate the recycle loop, but none were satisfying. Then it hit us. Asset transfer was unnecessary. Simply keeping a balance of BTC and USD on two or more exchanges and buying and selling as prices converged and diverged yielded essentially the same result as a traditional pairs trade. By the time we had fixed the recycle loop issue, the opportunity for interexchange arbitrage evaporated.

Those juicy gaps between exchanges all but evaporated. Reinvigorated, we tweaked our code and killed the villainous recycle leg. We finally had it but were too little, too late. By the time we modified our code and backtested the new strategy, the opportunity had evaporated.

A serious arbitrageur could surely pick up some of those Washingtons ahead of the crypto steam roller, but we were out. Bellman-Ford Undeterred, we knew that we were only beginning. Just because the most obvious arbitrage opportunity on the planet dried up did not mean that institutional sharks had gobbled up all of the free money that crazed retail investors had left on the table.

Earlier in our exploration, we read a post about intra-exchange arbitrage on decentralized exchanges. One only needs to hit them both at the same time to harvest risk-free profits interestingly, these strategies still exist if you play with gas prices! Unlike the interexchange arbitrage example above, creating an interexchange arbitrage strategy was much more challenging. It is impossible to identify opportunities by eye.

Collecting enough data and writing a backtest would take as long as writing the actual system, and much of the risk lies in proper order execution. And we knew that we were strapped for time. We saw the interexchange opportunity collapse in a handful of months. We cobbled together a system using our old HTTP-based order execution module, some devilishly complex data processing steps, and the networkx graph analysis library and unleashed its might on GDAX.

Nothing happened. A broad spectrum of cryptocurrency exchanges exist. On one hand, there are professional outfits like GDAX and Gemini that are based in the US and have taken serious institutional money to build a serious product. On the other, there are shady decentralized exchanges like EtherDelta that are so bug-ridden that executing a single order can be a challenge in itself.

In addition, the exchange needed a stable API and ideally would not instill fear every time we deposited currency. Liquidity appeared to be real. We tore out our old execution engine, replaced it with one powered with ccxt , allowing us to abstract away individual exchange peculiarities, and set Bellman-Ford to work. Watching the initial trades like a hawk, we were blown away. Despite making our requests from a server in the US over HTTP with an 8-second roundtrip, we were profitably arbing a major cryptocurrency exchange.

There were several obvious problems with our system, the first being speed. Our code used HTTP requests to fetch complete copies order books from across an ocean. This is the equivalent of sending a pigeon from Chicago to NY to report price movements.

The fact that we were able to profitably trade 8-second-old opportunities was just loony. Modern trading firms co-locate their servers with those of the exchanges to reduce the time between receiving price information and executing a trade based on that information. Through some handy profiling experiments, I determined that HTTP request latency was by-far the largest driver of our 8-second roundtrip time.

This meant that we made hundreds of requests to assemble our graph. While a detailed exploration into the benefits of async vs. Profile of the top most time-consuming functions on a German orange and US blue server. In this profile, the first two run slightly slower because of a poor choice of hardware. We knew that Hitbtc was British, but a little network snooping taught us that their exchange servers were located in German DigitalOcean datacenter.

Fortunately, AWS opened a region in Frankfurt, so we could experiment with pseudo-colocalization by simply moving our code there. A couple clicks in the AWS console later, we were trading live with a 1. Our German server retrieved all of the tickers in less than a second. Profits awaited! We thought that our problems had been solved. Initial experiments showed that we were running 5x faster and finding arbitrage opportunities every few minutes.

As our trust in the system grew, we ran it for steadily longer periods of time with steadily less supervision. Nothing was more satisfying watching free money announce itself in the console as a reward for our mathematical guile. After a few weeks of progress and debugging, we were ready for an unsupervised weekend. Friday night, we started trading and pledged not to look until Sunday. When Sunday arrived, I thought I had made a mistake. How was this possible? We only traded 0. What crazy bug was still lurking in our system?

Thus began a solid month of debugging. While debugging, we would periodically turn on Bellman-Ford for a few hours at a time. Sometimes we made money, sometimes we lost it. A clear pattern never emerged. Finally, we solved the mystery. A new type of logging enabled us to compare the expected and actual return of each trade in percentage points. However, many trades slipped below, reducing our profit potential.

In some kind of twisted Pareto distribution, the vast majority of our trades executed as expected this also contributed to the difficulty in debugging , a handful slipped but remained profitable, and few dozen lost money. Unfortunately, the distribution of trade size was reverse that of trade volume, i. Expected profit versus actual profit. Assuming perfect execution, all trades would fall on the dotted red line.

Histogram of trades and their actual profitability. The vast majority of trades are profitable, but the outliers ruined us. Upon further investigation with our new logging tools, we identified a pattern. Arbitrage opportunities arose when a large order came through a tail currency. If we snapped it up first, we could close the loop and make money. As before, the game ultimately came down to speed.

While in early January we were able to profit with 8 seconds of latency, 1. If we were able to speed up our system to the speed of the exchange itself, that prize would be ours for the taking. We had a need for speed, but neither of us knew where to start. Articles on optimization began dominating my browser history. Each HTTP request was taking about ms from our German server, but all crosses were taking nearly twice that. In theory, through multiprocessing and multithreading, we should be able to drop the total time to that of the longest individual request.

This would give us a solid milliseconds. On the processing side, we had a few slow steps. We assembled the graph using Pandas, which is slow as molasses and ran graph processing on numpy and networkx. It was dead obvious we needed to change the former. Pandas is nice for analysis, but switching the pipeline to numpy would easily give us a x improvement and ms. The latter two were trickier. Many hours later, our optimized code was ready for experimentation.

We had dropped the round trip time to an average of ms by running multithreaded requests across multiple machines and excising the slow Pandas operations. At ms, some of our trades were still slipping. We needed to do more. As a brief aside, there is not much written either online or offline about optimizing HTTP request speed. In addition, writing multithreaded code is much easier than asynchronous code. I wish I kept more careful notes, but I ran experiments comparing multithreading with async across multiple processors and multiple machines.

I found that the multiprocessing library almost always slowed things down, but multiple machines 7 using multiple threads and interacting over HTTP flask were able to reliably fetch the order books faster than any other combination. Websockets had been top-of-mind since the beginning.

Pulling data from a real-time stream will always be faster than periodic snapshots, but our beloved ccxt library did not yet support websockets, I had literally zero experience in the area, and we would need to write a bunch of complex logic to create the order book itself. Each Hitbtc HTTP response contains a complete copy of the order book levels at every price , but each websocket message merely transmits whether market order was filled, a limit order placed, or a limit order canceled.

The trader is responsible for maintaining a local copy of the order book and not missing a single message. Missing even a single market order could result in an out-of-date local order book, which could trigger a catastrophic sequence of money losing trades.

Imagine that the local book missed a message for a cross that rarely traded, meaning quite a bit of time passes between messages. If a theoretical arbitrage opportunity emerged from that missed message, i. For our little operation to have any hope of competing with the other sharks on the exchange, we would need to move at the speed of the exchange. However, the green and red shaded areas representing short-term buy and sell zones are asymmetric with relation to the median price.

The sell range begins at 0. Short-term trading with these ranges would be more likely to result in accumulation of shares than the "Maintain Share Count" strategy discussed previously. Accumulate Shares Author The only difference is the buy spread has been reduced to 0.

The recommended sell orders are identical while the recommended buy orders are slightly more aggressive. A good Seeking Alpha article on wash sale rules is also available. Generally, IRS wash rules apply to the sale and purchase of an identical stock within a day period and are most often applied to tax loss sales. Another scenario can be beneficial to investors.

If a losing position or fraction thereof is sold and then repurchased at a lesser cost, a wash sale occurs. Most brokerages will adjust the cost basis of the position. I swing trade losing positions aggressively and have shared my strategy in comments on BABA articles on two occasions. Risks: Plan to Win but Be Prepared to Fail A thorough fundamental analysis of BABA or discussion of news is beyond the scope of this discussion; there is no shortage of analysis and news elsewhere.

If one is going to buy or sell any stock, including short-term, evaluation should be carefully considered. Further, the recommended buying and selling ranges may not be advantageous over any period if BABA makes a big move in either direction. Short-term trading can go wrong in at least two ways. An investor can sell a fraction of a position or an entire position at the beginning of a sustained rally. That investor might not have an opportunity to repurchase those shares at a lesser price if the stock continues to rally.

Conversely, one can purchase an unlimited quantity of shares on a dip and never be able to sell those shares for a gain. An individual investor can manage these risks several ways. Complimentary but opposite limit orders can be placed as transactions close. Some brokerages have special order types whereby a secondary order is placed after a primary order executes.

Further, an investor can limit how much of a position is sold or stop buying a dip once a position limit is reached. Both selling and accumulation can be limited by adjusting the frequency, size, and spread of the trades. I recommend investors do their own scribbling over a price chart digitally or otherwise and then tabulate limit orders. Investors who already hold a larger BABA position than fits their own risk reward profile may be eager to reduce exposure. Don't run. Investors who patiently execute carefully considered plans are often lucky.

In the moment of action, remember the value of silence and order - Phormio of Athens This article was written by Followers Follow I most often base my analysis on company fundamentals, industry specific data, and broader economic trends. I read company quarterly presentations, but very rarely cut and paste presentation content and include it my analysis. Those presentations are put together specifically to present company data and results in the most favorable way limited only by SEC regulations.

I have not seen a single company presentation advising investors to sell. I sometimes work with fellow Seeking Alpha author Badsha Chowdhury. I wrote this article myself, and it expresses my own opinions.

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