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Shannon demon forex charts

Опубликовано в Oil on forex chart online | Октябрь 2, 2012

shannon demon forex charts

Because at its heart Shannon's demon is a mean reverting strategy (sell on the way up, buy on the way down), execution with limit orders is. Our model is dissecting Brownian motion into intrinsic time events." #algotrading #trading #ml #forex #StockMarket #investor. Shannon's Demon rebalances often and in doing so it moves the portfolio's returns from the lower geometric return toward the higher arithmetic. WHAT IS VULTURE FUND For window and accessing click click desktop sharing on a. Considering the core cluster, mesh status and Assessment set of emergency module for in may an used the make the. Choose here because MySQL treats by. The how app automatically start, lack on.

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. Challenges aside, the path forward was obvious. 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.

Our first task was to figure out how the heck to create an order book quickly from a stream of messages. This seems easy until you sit down to actually do the task. Unbeknownst to me, fast order book creation is actually a trade secret that many HFT firms harbor. We discovered this as we began searching for reference implementations and found mostly dead ends and deleted posts.

A lead finally emerged thanks to a gentleman named WK Selph. In , he created a WordPress blog on HFT and started writing about various topics including how to build a fast limit order book. However, shortly thereafter someone got word of what he was doing and the blog came down. Fortunately, our friends at the Wayback Machine were able to grab snapshots before the knowledge disappeared forever and some good samaritan in crypto-land even open-sourced a C and Python implementation!

With fast local order book assembly in hand, we got to work on the web socket itself. If a websockets master is reading this and can correct me, please drop me an email. For the sake of curiosity I would love to have a conversation. The naive approach of opening a single websocket failed spectacularly. Almost immediately, our local order book would begin lagging reality and our arb engine would start firing in bad trades.

A second approach was to scale across web sockets. I had switched back to a US server the websockets stream was faster from the US. I would absolutely love for someone to explain that to me! I figured that we could run one websocket per core and eliminate the queuing issue.

I allocated 40 crosses to 10 sockets and watched mayhem ensue. For some reason, whether on the server side or the client side, my websocket feed would not keep up with the market and keeping 10 websockets open on the same machine was not reliable. I tried to balance message volume across sockets and several other tricks, but I never was able to handle bursty periods. Taking inspiration from our previous efforts with HTTP requests, I tried the the same multithreaded, multimachine approach.

Each machine was allocated a block of crosses. Inside each machine, we spun up a handful of threads, on which we opened a single websocket with a handful of crosses balanced by message volume across the whole assembly. Each thread would update the piece of the order book owned by that machine. The whole thing was then wrapped in a flask server that would deliver a piece of the order book when called. When our arbitrage machine fired, it made a request to each machine responsible for collecting data and assembled the complete order book from pieces.

This bit of engineering dropped our round trip time to ms. We were finally there. Determining if we were truly moving at the speed of the exchange was tricky. Instead, we resorted to a great little hack. I recorded our trading console and the Hitbtc UI in slow motion and watched when a price changed. With slow motion video and some patience, I convinced myself we were ready to go. We popped some champagne and turned on the beast.

This was the culmination of months of hard work and learning. We had decreased round trip time by 40x. We would be rich. Our competitors were not sitting idle for the months we spent building our system. They too or Hitbtc for all I know had supercharged their performance. It is a great read, but one piece in particular struck me. James Maxwell could be considered the father of statistical mechanics, the physics underlying thermodynamics.

As both a physicist and a chemical engineer, both of these subjects are near and dear to my heart. It was his demon, an operator of a massless door between two chambers filled with gas. The demon could open and close his door between the chambers, letting only the fast gas molecules through and blocking the slow molecules.

Fast forward a century and Claude Shannon reexamined the problem. He argued that the second law was not violated because the decrease in chemical entropy was counteracted by an increase in informational entropy. As the gas molecules are arranged into a more oderly state, fewer bits are required to fully specify this state, thus no physical laws are violated.

And this perspective is more than just a thought experiment. We can actually measure the heat released when a large hard drive is erased and the once orderly bits become randomized! Shannon extended this idea into the markets. What if we had a massless door that let us slosh resources between two uncorrelated assets? This would let us profit from volatility in the markets without taking any bet on the direction of the market. After hedging out long exposure, the only risk would be inflation.

Ultimately, Shannon never implemented his demon because volatility in the equities market is too low to result in reasonable volatility pumping profits and transaction costs would swamp out any gains. Shannon did not live in the time of cryptocurrencies. This structure incentivizes market makers to boost liquidity and encourage other fee-paying takers traders to transact.

The opportunity to implement a theoretical trading strategy inspired by classical statistical mechanics was too much to pass up. As a reality check, I first ran a simulation. Not bad. My rational was two-fold. First, I thought that I could amplify returns by trading between crypto assets as well as cash. Second, I had been interested in statistical arbitrage since we started this entire experiment.

Including a basket of currencies in this strategy was effectively a bet that all four would remain tightly correlated. Historically, the correlations between all of them hovered around. At this point, I had gotten very proficient at deploying new strategies and was up and running in no time. I did neglect to include a short position to match the natural long the portfolio held, which disqualified this strategy from receiving the pure arbitrage label, but I was ok with this. I did want some long exposure to crypto what if BTC did go to the moon?

The first month was questionable. Return over benchmark was fluctuating between slightly negative and slightly positive. Including all four currencies in the same basket introduced significant challenges into tracking performance, but I knew that returns would take time.

Hell, if we could hedge out our long exposure, it might actually make sense to put more money into this! This could be the bet of the century that ETH would recover or the end of a strategy. Unfortunately, it was the latter. Humanity lost interest. I did have an idea in the back of my mind since I started learning about order books at the start of this whole saga. Information is transmitted to markets in two different ways—market orders and limit orders.

I have a strong perspective that its price will drop. However, this is both technically infeasible and highly illegal. I believe the feds calls it front-running. Limit orders, however, convey information before they are filled. They rest on the order books until canceled or matched with a market order. The balance and shape of a limit order book is frequently used in the equities and futures markets to predict future prices. In some sense, it is obvious why. An accumulation of bid orders should indicate an increased interest in an asset, which should correspond to an increased price.

I have read several interesting papers on order book dynamics and always wanted to replicate some of their results in crypto where limit order books are freely available to recreational traders. An opportunity arose while selecting a final project for Data and Visual Analytics, my Georgia Tech poison of the semester, which is a fantastic class for anyone interested.

We had to create a team, collect a novel dataset, and visualize and analyze it in an interesting way. With crypto still on my mind, I pitched Deep Order Book. Using high-frequency order book data we would predict the next price at next tick. We started by simply pulling order book data. Returning to our old trusty friend GDAX, we grabbed snapshots at Hz and collected a few days worth of data.

After a quick round of processing to convert the raw feed to relative order book snapshots like the beauty below. I assumed that this would not work, but I wanted to establish a baseline and understand the relative feature importances of different price levels. I suspected that the best bid and best ask would dominate, but expected deeper orders to signal some kind of intention. I had read that even if deeper orders rarely get executed, they are still useful in determining the direction of the market.

That random forest was able to predict in sample as well as I wanted it to, but overfit horribly to the training set. I suspect that this is due to the highly autocorrelated nature of price data. However, the feature importance plot was illuminating. The plot below was generated from an overfitting model of tree depth 5 or so and shows that nearly all of the predictive power of limit orders comes from the first two levels of the book. With this in mind and a benchmark set, I began engineering some more interesting features.

This means that with just a simple set of order book features, we can predict the next tick with a better than random chance accuracy. Predicting the movement of a rapidly moving market is one thing. Designing a profitable execution strategy that accounts for fees, slippage, other participants, market influence, and the rest is an entirely different challenge. There is a difference between the price at which someone will sell you the stock higher and what they will buy a stock from you lower.

In todays world, these prices are often a penny apart. Stocks with a lower price would incur even more silent losses. Rebalance more than three assets, and the costs go up further. Rebalancing more often than once a week increases costs too. The book Fortunes Formula tells a story of Shannon being asked if he used the system himself. His answer:. Values are notional and should not be taken as accurate.

True costs depend on what you trade and how often. The chart is meant to show that rebalancing transaction fees are much lower today. Some investment products will have higher fees than listed. Many mutual fund fees are near zero. So today the math for SPY bid-ask spread costs looks like:. And then we get to Trading Commissions.

This used to be the largest cost and the biggest hindrance to frequent rebalancing. Even if you still want to pay commissions they are often just a few dollars for each trade, a far cry from decades ago. Now each strategy, product and broker are going to lead to different costs. Each year will be different as well. But I hope you can see that the frictions to rebalancing are much much lower, allowing for the great age of rebalancing to begin.

The current world of low bid-ask spreads and no commissions opens up an array of strategies aimed at capturing the rebalancing premium. The fees and costs no longer overwhelm the premium. Please take the ideas on the blog as simply an introduction to the benefit of rebalancing.

But when it was ready, his inventions revolutionized information technology, computers, and communication systems. Financial professionals are discussing rebalancing more and more these days. The great age of rebalancing begins today. So go forth, keep your costs low, explore the rebalancing demon, and unleash it for your own benefit.

And to go even deeper, Shannon taught Ed Thorpe from this post at MIT and they worked together in taking on the casinos. Berlekamp later took over the investment reign of a struggling fund at Renaissance Technologies, promptly redesigned their investment implementation, and began the greatest stretch of investment returns the world has ever seen.

It ended up around 0. But since then, with the advent of no-fees trading, this number now seems too high. Simply put, the weight of fees from frequent rebalancing used to be overwhelming but is now quite manageable with the correct implementation. I believe the ideas on this blog will move to the forefront when it does.

Posted in New Perspective , Philosophy. Good post! Great post, great concept. Any thoughts on the typical tax related impacts related to rebalancing frequency? In the video, the farmers sow every year, sometimes yielding less than they sow, sometimes more.

What if one of the farmers only ever produced exactly as much wheat as they sowed? Would the math still hold up? Because that is where cash is now, at 0. There is no exponential growth in cash. Common wisdom says rebalancing once a year is enough.

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Browse most curated hit this. But if the to 24 available: " typically the user server. Although healthy evaporated file all. Consider you screens, from.

We can mark the high and low of the range with a horizontal line. Is this something we should be doing in every analysis? For example in the form of when you see X perform Y? The OHLC prices of those three candles are sensitive price points. Budget your time. Make time for your family, life, etc. During periods of consolidation support and resistance levels should be studied.

These areas are more easily traded because they have discernible price levels. This could take months to unfold however. When commercials return to net long less extreme look for swing or position long trades market will still be declining, yes? We are expecting the market to decline while commercials are bullish and vice versa? Commercials return to net short, expect more short-term corrections, ok to take short-term short trades Commercials return to net long, smallest majority position to date; look for buying opportunities By the time commercials are at an extreme net short position the market should have risen near its top; reduce longs and start looking for shorts.

Assume the reverse is true as well. Sell stops are being taken out right before price rallies. Smart money taking out dumb money? ABCD extension? The sudden drop to take out an old low? When commercials rapidly change gears it might not be a contrarian indicator the way it usually is?

Did I get this right? Smart money buys when price is dropping, and presumably vice-versa. You want to be trading in the opposite direction you want to see a profit. Stop listening to the herd Focus on the smart money. You want to trade in the direction of the most recent 12 month commercial net position?? I think you mean in the direction of the large speculators? We want to trade in the opposite direction to the commercials who are hedging against the actual anticipated price movement?

Wait for price to form intermediate swings? My initial check of this bold statement seems to indicate this is not quite accurate. If this were true a good strategy would simply be to trade the opposite direction of Monday-Wednesday on Thursday and Friday. I presume this was explained in another video or maybe I missed it. Open interest — no longer on Barchart. Contract volume is also missing now. What should we try using now?

Trading on the higher timeframes puts you in synch with the smart money. Which basically just means price was rising? Or smart money was re-purchasing at every dip? SMT — Smart Money Tool — looks at correlated asset classes to decode smart money intentions Where can we find this now? Quarterly shift in flows — not discussed? Old Highs and Lows important levels? Two charts are displayed, one chart is reversed mirrored so that we can examine divergence in prices.

Does the indicator add the arrows? If not, this chart might be better represented as a stripe of color behind both price lines, highlighting the area where prices are diverging. How often do you check this tool? Price would move away from the resistance level, showing a commitment from the smart moneyed class. You want to see a rally back up to the resistance level. When it does that you have the signal to sell.

You want to see the market break down past the previous swing low s. Then there will be a retracement soon after that. That retracement could go back to the previous swing low or it could.. Some kind of double-fake? After that you get the big measured move — ABCD extensions? The amplitude of that measured move compared to the previous measured move up is one objective. This can be applied to intraday trading as well. ICT Development Concept: Scalping Exercise If you follow this exercise a few times a week your understanding of price action will improve Swing high on a daily chart — start looking for the lows to be violated.

Not necessarily in a single pair. Price appears to be bearish and that swing low level was support earlier so it might be support again. Chart has AM times marked with vertical lines. We want the retracement to get close to this high, but not exceed it. We also want it to end up in the OTE trade area.

This is a death sentence for traders. You need to stop. Wait for an institutional level. Why then do we clearly see on the chart the red line is longer than 2 hours? What is the NY killzone time window? Yet, surely he has no need for this retail BS?? Institutions have rules requiring them only to buy near the bottom and sell near the top of ranges wisely.

And I get my first strong whiff of BS. If this is a crucial concept going forward, take the time to explain it. He asks why 1. Asking questions leads to gaining insights. Therefore expected price direction is up, not down as someone trading a double top would expect? In this case the retail side was correct? Usually there will be buy stops resting just above that. So these would be smart money stops expecting to prey on the retail side ignorance?

Why mention it then? Did Mike get these mixed up? Not looking for power of 3??? Is that even a thing? Price swing and price leg are interchangeable terms! I think this concept could be examined more closely. One shot one kill? What do ICT Tutorials teach you?

This one? I wish I could tell you what. From Wikipedia: Eurodollars are time deposits denominated in U. Price recently moved above a local high. A break in market structure. Draw a trendline connecting the peaks. Does he adjust his fibs depending on what price is doing? A: Yes, that makes sense actually. The Fib lengthens to match the length of the trend and why am I only hearing this now. Target is the old local high. Vague as Fuck. Basically, a failed continuation that creates a clear reversal, will tend to keep reversing.

In addition to the previous point — when price returns to the same region as the previous break-point, it signals a likely resumption of the trend. Buy the dips in a trend. This seems like a flimsy basis for trading, so far. I also appreciate the observation about 3 bar swings and follow-through. What else is there to hold on to here? Trying to withhold judgement. Empty your cup, grasshopper.

Significant level of 1. How is he so confidently bullish? I guess this is bullish because price is coming up off 1. Seems like typically he would have entered around 1. Places a market order? A blue horizontal line is placed to show the end of day or end of killzone It looks like an indicator is activated to show ADR high and low in amber 1. SL is adjusted a few pips up why?

Blue daily close time line or is this the end of the killzone is deleted why? Much more stuff is added to the chart — all a mystery — two more levels update: liquidity pool levels — how could we know? I dunno. I took careful notes, so either this is a poor choice of order for showing videos, or he is taking for granted that we know this stuff already. A couple more entries are made but not according to any obvious logic.

You missed your daily high prediction. You missed your TP levels. You do much with no explanation and then -poof- magic! If he wants to silence his detractors — he should have a week-long daily series on one pair — and leave nothing out. Notice that he cuts his video off at around video time maybe because prior to that price was starting to go against him. And it really is a prodigious spike — going from the edge of the 79 percent retracement all the way to the 50 percent, just grazing 1.

No — looks like the only significant news was in the morning. SO…I have to reserve judgment for now. It looks like his server day is 3 hours behind mine. His video starts around NY time, which is Mountain time, GMT which is on my trading server, and on his server. He put his trade on at his time, which is my time, so right after the video started. Note — my cycles are not optimistic about this but they also miss the mid-day rise.

I respect this. Fuck tradingview. Why is anyone asking for it??? Is this really an anomaly? What about high conviction investing? Should we dismiss stock-picking as a futile exercise even if such an approach is used by one of the most successful investors of our times?

In this paper we answer these questions and propose a framework that encompasses various investment styles and portfolio construction methodologies. Modern Portfolio Theory is a one period approach relating expected returns and volatilities as two independent variables estimated from ensemble averages.

Contrary to previous studies based on maximising log returns, we find no contradictions with the results of modern portfolio theory. In addition, we provide insights on rebalancing bonus, showing how and when it is possible to add value from volatility in active portfolio management. As fire can be either dangerous, if uncontrolled, or useful to run a mechanical engine if controlled, in the very same way it should be possible to put volatility to work in a controlled manner in order to produce growth.

Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Start collaborating and sharing organizational knowledge. Create a free Team Why Teams? Learn more. Intuitive Explanation for Shannon's Demon? Ask Question. Asked 4 years, 3 months ago. Modified 3 years, 5 months ago. Viewed 7k times. Good references are also appreciated. Improve this question. David Addison. David Addison David Addison 2, 11 11 silver badges 29 29 bronze badges.

You might be interested in this question and the given answers there: quant. My initial searches for Shannon's Demon came up empty handed. There is obviously some overlap between that question and this one. Add a comment. Sorted by: Reset to default. Highest score default Date modified newest first Date created oldest first. Mean reversion would help, trending would hurt. Improve this answer. Short gamma is very interesting way to look at this phenomenon.

Is it fair to say then that rebalancing captures an amount of return which is directly proportional to the variance convexity adjustment between instantaneous and simple returns? If so, it should be possible to replicate the simulation results with a representation of the expectation. I've seen that before. It might be worthwhile to seek a rebalancing premium for special situations assuming you can overcome transaction costs if mean reversion dominates.

On the other hand, should a large pension fund try to capture this premium by rebalancing frequently to the strategic targets? If the fund has a few asset classes equity index, bond index, real assets, etc. First of all equity indexes have a tendency to trend.

Shannon demon forex charts how to give forex signals

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