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gnarsed
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ridge/rida down small. rief up.
what factor moves did you think would be most painful? |
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gnarsed
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| Total Posts: 95 |
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ridge/rida down small. rief up.
what factor moves did you think would be most painful? |
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leftskew
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| It has been a while since I analyzed last, but I thought they had exposure to the anti-beta/low vol one and quality, and I don't think those factors have performed well recently. But I wouldn't be surprised if they were making changes. |
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zee4
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| Are these numbers YTD or Feb only? Regardless, -5.87 and -7.86 do look ugly. |
Бухарский |
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chika
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These are YTD performance based on a few funds I track
what is the data source for this table? |
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zee4
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| Looks like HSBC Global Markets report to me. |
Бухарский |
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elf
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| RIDGE is down 1.6% in Feb, down just under 7% YTD. |
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jslade
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https://www.quora.com/How-reliant-is-Renaissance-technologies-on-HFT
Appending here for the record
James Baker, same math as Renaissance Tech from same source
Updated 5 years ago · Upvoted by Tom Groves, One of the founding partners at LindenGrove Capital, a
Macro hedge fund and Jack Wei, Hedge Fund Analyst · Author has 200 answers and 4.7M answer views
What are the investment strategies of James Simons/Renaissance Technologies? I
understand he employs complex mathematical models, along with statistical analyses,
to predict non-equilibrium changes.
Originally Answered: What are the investment strategies of James Simons/Renaissance Technologies?
I have known Jim Simons, Bob Mercer and Peter Brown since 1965, 1974, and 1979,
respectively. Renaissance has also hired senior researchers who had formerly worked for
me for years. None of these people has ever told me anything about Renaissance's
investment strategies. My observations below have been obtained entirely from publicly
available records.
In particular, the core strategy is publicly known. It's the details that are proprietary. There
are millions of details, and they are essential to the performance. However, the question
was about strategy, so that is what I will try to answer.
The core strategy is portfolio-level statistical arbitrage carried to the limit and executed
extremely well. Basically, portfolios of long and short positions are created that hedge out
market risk, sector risk and any other kind of risk that Renaissance can statistically predict.
The extreme degree of hedging reduces that net rate of return but the volatility of the
portfolio is reduced by an even greater factor. The standard deviation of the value of the
portfolio at a future date is much lower than its expected value. Therefore, with a large
number of trades the law of large numbers assures that the probability of a loss is very
small. In such a situation, leverage multiplies both the expected return and the volatility by
the same multiple, so even with a high leverage the probability of a loss remains very small.
The general properties of the strategy can be deduced from the statement of Renaissance
for the Hearing of the Senate Permanent Subcommittee on Investigations, dated July 22,
2014.
[https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=6&ved=0CEcQFjAFa
hUKEwjVl_LOifrHAhWJ1h4KHTBAAj8&url=http%3A%2F%2Fwww.hsgac.senate.gov%2Fdow
nload%2F%3Fid%3Db81368f4-6448-4867-8572-f2340418d029&usg=AFQjCNGlNbiS9DOE_N
jQ8Ks_BT_X54dIPw&sig2=s0RdwL0a8afNv6lUBfyxVQ&cad=rja]
Renaissance collects "all publicly available data [they] can that [they] believe might bear on
the movement of prices of tradable instruments--news stories, analysts' reports, energy
reports, crop reports, weather reports, regulatory findings, accounting data, and, of course,
quotes and trades from markets around the world."
Their models "use this data to make predictions about future price changes."
The hearing was specifically about the Medallion fund, about which the statement says "The
model developed by Renaissance for Medallion makes predictions that are profitable only
slightly more often than not."
With these properties, there were two reasons that Renaissance would like to have a call
option on the portfolio that it has designed: leverage and protection against Black Swan
events.
Leverage is needed because, unleveraged, the rate of return of the portfolio is low. However,
because the volatility is much less than the expected return there is no limit to how high the
leverage could be without increasing the probability of a loss, at least according to the
models. Through years of use and refinement, Renaissance knows that its models are very
reliable. However, they also know that there is always the risk of something happening that
is not covered by the models, in particular something that is outside prior experience, which
is called a "Black Swan" event.
Thus, a call option is ideal: it can provide high leverage and can provide protection both
against the very low probability of a loss greater than the option premium and also against
the unknown probability of a possibly catastrophic loss due to a Black Swan event.
We know all this because these are the business reasons for Renaissance accepting
Deutsche Bank's proposal of barrier options. Basically, Deutsche Bank, and later Barclays,
sold the equivalent of a call option to Renaissance on the reference portfolio that
Renaissance designed.
Of course, writing an uncovered call on the Renaissance portfolio would be equivalent to
betting against Renaissance at high leverage, which would seem to be a foolish thing to do.
The banks covered these options by buying all of the securities in the portfolio. Thus the
bank's position was equivalent to a covered call. In other words, the banks' profits and risks
were essentially equivalent to writing a put option, which is a bullish position. Because the
volatility was very low the probability of a loss for the bank was low and the probability of a
loss greater than the option premium was even lower.
Except for the Black Swan risk. The probability of a Black Swan risk is unknown. Part of the
premium paid by Renaissance and earned by the banks was equivalent to insurance against
Black Swan risk. I don't know if the amounts of the premiums were publicly disclosed.
There were many more details in the statements and the testimony at the hearings.
However, discussion of further details would detract from the important points that I have
made above. In particular, the hearings themselves were about tax issues not about
investment strategies. Renaissance explicitly asserted, under oath, that its "models do not
factor in tax rates when making trading decisions." Therefore, tax issues, although they
might be very important, are not part of the "investment strategy" at least as reflected in the
models, so they are outside the scope of this particular discussion.
[Edit (added in answer to a comment): The reference portfolio was highly dynamic. There
were thousands of trades per day. To accomplish this, the banks gave RenTech's computers
direct access to execute trades through the banks' trading desks.
This arrangement was part of what created controversy about what should be the proper
tax treatment for this particular case. However, I am not a tax lawyer and will not try to
analyze those issues. However, if you want to hear more details on the automatic execution
of the trades, and questions about how much human interaction was present, that is all
discussed in the live testimony before the subcommittee: [Hearings| Homeland Security &
Governmental Affairs]
Are the consistent high annual returns of Jim Simon's RenTech Medallion Fund due to
their ability to execute trades at high frequency?
Yes and No
Yes, Medallion Fund does do hundreds of thousands of trades per year. In some cases they
trade in and out of a given security position within seconds, though some positions are held
much longer.
No, in the sense that Medallion's success does not appear to be due to them executing
trades faster than others so that they can get ahead of price movements caused by large
institutional trades, which is the practice that causes "high frequency trading" to be
controversial.
As Sundeep Bhat has said, no outsider knows the details of what they do. All employees
sign a very strong non-disclosure agreement that apparently no employee or former
employee has ever violated. However, the broad strategy of what they do can be deduced
from public statements by Jim Simons, the founder of Renaissance Technologies, and Peter
Brown, one of the current co-directors. This includes Peter Brown's testimony under oath at
a US Senate hearing.
Basically, Medallion Fund does statistical arbitrage. They gather enormous quantities of
data of all types and use statistical prediction to find instances in which one collection of
securities or commodities can be hedged against another. An example used by Peter Brown
is based on the statistical observation that markets tend to do slightly better on days that
the weather is fair than on days when the weather is bad. Thus, if the weather is fair in New
York and bad in Paris, you can statistically make a profit by being long on securities traded
in New York and simultaneously short on securities in Paris. They do this even if their
predictions indicate that the market in Paris will go up, but merely less so than New York.
This only works if the securities in Paris are precisely balanced with securities in New York.
They won't be the identical securities, so it will be a very complex statistical balance
involving many securities or commodities.
However, this weather effect is very small. The system works by finding a very large number
of such small opportunities. In addition, hedging very different entities against each other
requires precisely balancing complex portfolios against each other rather than hedging
individual securities. That's the only way to get precise balance. They have a program with a
million lines of code to manage all this, and, yes, they do thousands of trades a day.
However, the large number of trades is a consequence of them needing to take advantage
of a large number of small hedging opportunities and having multiple securities and
commodities involved in each hedge.
Thanks for the A2A.
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"Learning, n. The kind of ignorance distinguishing the studious." |
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jslade
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Brandon Smietana, They call me "Flash Crash"
Answered 10 years ago · Upvoted by Andy Manoske, Associate at GGV Capital and Martijn Sjoorda,
Former COO asset management firm · Author has 495 answers and 1.7M answer views
How likely is it that Renaissance Technologies is a Ponzi scheme? Given how large an
outlier their main fund (Medallion) is in terms of extremely good performance over
many years?
Originally Answered: How likely is it that Renaissance Technologies is a Ponzi scheme?
Renaissance Technologies is not even the most profitable quantitative fund. These funds
are all doing so well that they do not take outside investors and do not want you to know
they exist. Its surprising that Renaissance Technologies has a website and releases its
return information at all, because most of these funds do not.
Renaissance Technologies is just one of hundred of there firms, although it is one of the
larger and older ones. However there are diminishing rates of return to on increasing
amounts of capital, so from that perspective Renaissance Technologies is not even
generating the highest rate of return for firms doing similar things.
When it comes down to it, the market is extremely inefficient. Up until a few years ago, most
trading decisions were being made by humans. We still do not even know how to price most
of the newer financial assets and there are still fundamental breakthroughs in mathematical
modeling of financial markets occurring yearly.
Most of the information in SEC filings and about companies is not used for pricing and
modeling by most firms. Most firms have no way to adjust the pricing of assets based upon
news reports. The majority of information that is relevant to financial markets is information
that we are only now integrating into our financial models.
Renaissance Technologies' 30% year rate of return is the kind of returns that someone using
a naive back-propagation trained neural network generating trading signals would get in
2002.
Today you can still generate great returns by taking something that a human trader once did
and doing it faster. For instance, by using NLP algorithms to process news reports and
using that information to predict price swings caused by news announcements.
A human might have an idea about the direction a stock will move and how fast, in response
to a new article. However the human cannot process the article and make a trading decision
in under 5 seconds, like we can with a computer algorithm. Also the trader has an intuition,
but is not going to be as accurate as the algorithm.
If we can quantify the price movement and integrate historical data on the past behavior of
the asset and other similar companies share prices responding to similar news reports,
then we can determine the likely magnitude of the price movement. We can also quantify
the uncertainty in our estimate of the magnitude of the price movement.
If you can process data faster, integrate more data or make more accurate predictions of
market behavior with better algorithms, you are going to see great rates of return. This is
merely what Renaissance Technologies does; they use data and mathematics to create
mathematical models of market behavior. It should be obvious that any firm with better
models is going to produce superior rates of returns.
Its also possible to arbitrage volatility and other statistical properties of the market and
many of these statistical properties of the market time series are still very inefficient. If you
want to see how inefficient the market is, you should do independent component analysis
on the market time series for the daily closing-closing price rate of return and decompose
the market series over the extracted factors.
Renaissance Technologies' performance is not surprising at all. They do not have any
finance people; they are a mathematician and physicist shop. Most of these people do not
even have enough finance background to construct a believable ponzi scheme.
The human fund managers I have talked to have told me that they prefer simple algorithms
that they can understand and which they can explain to their investors, over algorithms that
more accurately model the market and which produce higher rates of return!
They prefer 'simplicity' to achieving high rates of return at lower variances! The question
should not be "How likely is it that Renaissance Technologies is a Ponzi scheme?", but
rather "Why are we trusting our retirement fund investments to fund managers using 30 year
old commodity quantitative methods?".
I think Renaissance Technologies is just one example of a trend in finance towards
automated markets where trading and pricing are performed by quantitative statistical
models instead of ad-hoc human reasoning and intuition.
Laurent Bernut, ex-Fidelity short seller, ex-Hedge Funds analyst, ex-CPA, algo trader
Answered 4 years ago · Author has 584 answers and 5.1M answer views
How can Renaissance Technologies make so much money from financial markets by
hiring scientists and/or mathematicians with no domain knowledge of finance?
Originally Answered: How can Renaissance Technologies make so much money from financial markets
by hiring scientists/mathematicians with no domain knowledge of finance?
I have never worked at Renaissance, so please take my answer with a grain of salt, but here
is a first hand story that could shed some light.
On June 22nd in NYC, my colleague, who is also ex-US department of Defense consultant
and myself, met with one of the foremost US experts on sonar detection (good luck finding
him on Facebook, LinkedIn). He is a physicist with multiple PHDs, geeky funny. His expertise
is signal processing.
It was one of the most refreshing experiences ever. He explained his world. I explained
mine. Cotes de Provence Rose, beer and wild berry Zinfandel helping, we tumbled down the
rabbit hole talking even about epistemology, the philosophy behind math.
His world, signal processing, bears uncanny resemblances with ours. We explored Bayesian
probabilistic determinism, which models (Gauss, Poisson etc) to apply to distributions, the
cost of false positives (think trading edge), arbitrage between time and action with sparse
data (confirmation). We spoke the same language. We were talking real problems: how do
distinguish signal from the noise ? How fast ? What is the cost of being wrong ? What is the
cost of being right ? Which statistical law applies to randomness ?
We entered a massive time distortion. We started around 2 pm and a couple of bottles
down the road, but then after what seemed like 5 minutes, we were hungry. It was 10 pm.
We could have gone on forever (*)
Compare this with glorified journalists, otherwise referred to as fundamental analysts.
●●●●●●“This is fairly valued”... life is unfair darling, so do you really think markets
are fair ?
“On a sum of the parts valuation”... Frank N. Stein zombie valuation
“Fundamentals are strong”... Make fundamentals great again...
“Long term story is still intact”... Some HF reality TV celeb says that about
Valeant by the way...
“On a DCF basis, our target price is +10% above current market valuation” ...
stop tinkering the terminal value to rationalise your subjective views
“top quality management” ... was also said about Enron, Bear Sterns, Kodak,
GM, Chrysler,
Too much B/S bingo, too much theory,
Bottom line:
Physicists approach the markets as a statistical problem. This is practical.
MBAs have too much untested theories in their head. It is costly and time consuming to
unlearn all that junk.
“In theory, theory and practice are the same. In practice, they are not”. Yogi Berra
(*) There is no way i could ever afford someone of that caliber; he charges something the size
of Liberia’s national deficit per hour. But, he wants to send his granddaughter to Mars and he
thinks our algo could be the right fuel, so i invited him to have fun with us. Maybe good guys
do not always finish last
https://www.quora.com/What-are-the-top-high-frequency-trading-firms :
Christina Qi, Partner, Domeyard LP
Updated 8 months ago · Upvoted by Marc Bodnick, Co-Founder, Elevation Partners
[In wake of the 2020 crisis, this list may change substantially. Will keep you guys posted.]
[Updated again February 2019. Original post April 2014.]
[If you’re a reporter looking to reference this post, please give credit where due and don’t
just copy me word-for-word.]
I was originally compelled to respond because many of the answers were outdated
(Infinium blew up, Knight had just become KCG Holdings, etc), and wanted to compile a list
of current HFT firms for everyone’s reference. These firms are not necessarily the largest by
head count or operating capital, but are generally known to trade the most volume (by
notional value) and possess the fastest and most deterministic response times.
I also thought it would be helpful to list whether they’re a prop shop or hedge fund. Note
that some hedge funds have a proprietary arm, and that most HFT strategies fall under prop
due to capacity constraints. For those who aren’t familiar, proprietary trading firms (or prop
shops) are internal and smaller in AUM compared to your “Top 10 Biggest Hedge Funds”,
and you’re probably trading the CEO and Partners’ funds. It’s hard to compare a prop shop
to a hedge fund due to strategy and AUM differences. Thus, there’s a natural tendency for
HFT strategies to be proprietary. However, we’ve seen a few HFT hedge funds sprout up
over the years, though they all reach capacity quickly. We’ve also seen more hedge funds
raise venture capital and PE funding recently, as well as a record number of M&A activity in
HFT, but I digress.
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●Virtu Financial (acquired KCG) - prop shop
Two Sigma Investments - prop shop that launched a hedge fund
Citadel - hedge fund with prop arm
Hudson River Trading (acquired Sun Trading) - prop shop
Optiver - prop shop
Quantlab Financial (acquired the prop trading arm of Teza Technologies) -
prop shop
Tower Research Capital (Spire Europe) - prop shop
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●Jump Trading - prop shop
Tradebot Systems - prop shop
Flow Traders - prop shop
DRW Holdings - prop shop
RSJ Algorithmic Trading - hedge fund with prop arm
IMC - prop shop
RIP since last update: Spot Trading
If you’d like a larger list, this article by Grainestone Lee is pretty comprehensive. Below is a
screenshot (sorry for the highlight over my company - I was very happy to have barely made
the list):
I confirmed with Jim Simons: Renaissance Technologies doesn’t do HFT. He got pretty
annoyed when I asked him. Perhaps it’s a common misconception about their firm.
(my note: Simons invested in her Domeyard HFT firm, so makes sense that it is a
diversification play for Simons, rather than a competitor; and that she would be able to ask
him directly).
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"Learning, n. The kind of ignorance distinguishing the studious." |
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| lol - i honestly thought it would be wayyy more complicated it sounds just like an expert system or like Watson which (Peter Brown & Robert Mercer did the ground work for) used multiple machine learning models and had a scoring system to pick the best answer. |
You going to carry that wieght? |
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James Simons, Robert Mercer, Others at Renaissance to Pay Up to $7 Billion to Settle Tax Probe
Tax settlement, which current and former executives will personally pay, may be the largest in history
[https://www.wsj.com/articles/james-simons-robert-mercer-others-at-renaissance-to-pay-7-billion-to-settle-tax-probe-11630617328] |
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Hey Guys/Gals,
I wanted to give some research I've done on what I think RenTech could be doing in broad strokes but a much better level of detail than all the public sources I've seen.
1. It seems they're clearly doing the classic quant fund "least squares regression over a factor model" to hedge out their risk of the entire portfolio. This will make you neutral to all factors in your model, as well as almost dollar/beta neutral. ( depending on your definition of Beta... )
2. They may be using, or did use a PCA model for some/all of their risk model. It's nice and clean because your risk model factors are orthogonal and you can choose how many factors to hedge since you naturally get any percentage of the variance you want explained by using up to N eigenvectors where N is the number of securities. ( They mention PCA being the breakthrough in their equities model in the book "The Man Who Solved the Market". ) Now there's many problems with covariance drift that they had to solve but I assume some sort of shrinkage or exponential weighting on the covariance matrix used to calculate the PCA?
3. Their signals are "auto-generated" or mined from the data and then put through a barrage of tests for "out of sample performance", "statistical significance", "correlation to prior signals", "model complexity" etc... to see if they should be added to the portfolio. The evidence for this is all hearsay but here's a nice breakdown in Hacker News:
Quote: "Renaissance Technologies has completely automated the process of signal discovery.[1]"
https://news.ycombinator.com/item?id=16649002
Also many firms do this already such as WorldQuant, Trexquant, etc.. They use Genetic Programming to generate and test millions of generated factor models and combine them together.
4. They combine these signals together with optimization algorithms, examples being the ones from scipy.optimize ( constraint optimization library ). This is so you can optimize around trading costs. A lot of your signals will have high turnover so you won't want to weight them too heavily as the signal is not strong enough to overcome the transaction fees but may be useful when it's blended with thousands of other signals.
5. I know this works because I've done it all and traded it on Binance embedded in a Market Maker and had a Sharpe of 15 ( no down days ).. until they booted me for VPN violations. ;)
Anyone have any other ideas? or email to chat? I have a bunch more stuff. |
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rod
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| Recently, David Magerman was interviewed by Will Wainewright. In this interview, Magerman talked mostly about his startup investments, but the few seconds on RenTec were interesting, e.g., the use of air-gapped computers. |
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amin
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| Total Posts: 309 |
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Friends, I claim that my machine learning prediction models are as good as rentech's models and I am looking for capital for my proposed hedge fund.
My preferred place and first choice is to set up in HK/China but I might be willing to start somewhere in mainland Europe if I can find a good deal.
I have put together some details about my models in following linkedin post. Please feel free to connect on linkedin.
https://www.linkedin.com/pulse/trading-venture-proposition-ahsan-amin/ |
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amin
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This is strange. LinkedIn has removed my article post and blocked my account. I have not received any email from linkedin about it whatsoever.
My latest post has been removed: https://www.linkedin.com/pulse/trading-venture-proposition-ahsan-amin/
Somebody probably complained to LinkedIn. I will post all the information in the post as such on this forum.
When I tried to change my password on LinkedIn, I was told that I needed to upload an identity verification document. I uploaded image of my passport and I later received an auto-generated message about the passport that my passport will be reviewed in 3-5 days. |
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amin
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| Total Posts: 309 |
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Friends somebody out of the blue complained and linkedin suspended my account without even giving me any notice.
Again I am looking to start a small Hedge Fund in HK/China though I can consider some place in mainland Europe if I can get a good deal. My email is anan2999(at)yahoo(dot)com.
You can call me on whatsapp but you have to send a message earlier. My mobile whatsapp is +92-336-2602125
Here is the content of post on linkedin at web address:https://www.linkedin.com/pulse/trading-venture-proposition-ahsan-amin/
Trading Venture Proposition.
Algorithmic Model Description.
Our model is close to high frequency trading models where trades take place roughly every thirty seconds to a minute.
Our model makes a prediction of the market fifteen seconds ahead and then makes decision to buy the stock asset if the market is predicted to go up and sell it if it is predicted by the model to go down. Once a trade has been made, we keep the trade alive after fifteen seconds if it is profitable to continue the trade and end the trade if it is not profitable to continue the trade.
Depending upon the market conditions, our model makes between six hundred to one thousand trades per day with average lifetime of a trade around thirty seconds to more than a minute.
We enter the trades through limit orders and earn a portion of bid-ask spread.
Our trading style and philosophy is similar to that of the most profitable firms on Wall Street especially Renaissance Technologies that makes billions of dollars in profits every year. We believe we have equally good trading algorithms.
Machine Learning Techniques Behind the Model.
Our model uses predictive machine learning to make trading decisions. We do research on independent factors that affect the short-term evolution of the financial asset and then apply machine learning to optimize for the parameters that best describe the short-term dynamics of the financial asset.
In our back-testing simulations, these optimized parameters are learnt from previous day’s data and are used to make trading decisions during the following day.
We tested our algorithms on large scale data with above rolling window of training on 24 hours of data and then applying the model to next 24 hours of data.
Our machine learning method is a new technique and is not known to people in financial markets. This results in more accurate prediction of the market than any time series model or process that analysts and quants usually use in financial markets to make a prediction of financial assets.
Calculation of Profits Associated with Our High Frequency Algorithmic Trading.
Author has experience with American stocks data and have previously run several algorithms on Nasdaq Stocks. However empirical calculations of profit and loss with this algorithm were done on major crypto currencies including Bitcoin and Ethereum using Binance data. Our algorithms consistently turned profits on both cryptocurrencies despite that they are far more volatile as compared to US stocks. Our algorithms consistently made profits in both highly bullish and highly bearish markets.
We used zero leverage in our projection of profits and costs.
In our trading back-testing, we made a profit of 1.5 cents per hundred dollars without including any earned spreads. Our models made roughly about one thousand trades per day as crypto market remains open for 24 hours. For high volume traders that trade Binance futures, Binance pays a rebate of one cent per hundred dollars. Adding our profits and rebate means we can make 2.5 cents per hundred dollars per trade on average. There are roughly one thousand trades per day taking our profits to (1000 *.025/100=25%) per day.
At this point, I will stop and reassure the reader that these profits are real and I can demonstrate them in independent back-testing. Such profits became possible due to our superior statistical machine learning model that correctly captures 20%-30% of all the variation in the financial assets.
When we applied our model on Cryptocurrencies, our Sharpe ratio was roughly 2.0. This is considered excellent Sharpe ratio with little downside risk.
In our back-testing simulations with Bitcoin and Ethereum there were several months in the data in which we made no loss during any single day of the month.
In the past, Author has applied vastly inferior models on Nasdaq stocks data and was able to find reasonable profits with those models. If this superior algorithm is run on Nasdaq stocks data, it can easily earn one cent per hundred dollars after accounting for round trip brokerage costs associated with every trade. This would result in extremely profitable trading strategy for Nasdaq stocks trading.
Again, very small but consistent profits when aggregated over a large number of trades become very significant and usually become several percentage points of the capital for even a single day.
Independent Verification of the Model
We fully welcome independent back-testing and verification of the claims made about profitability of our trading algorithms. Though we would not be willing to reveal machine learning techniques behind the algorithm, there can be several ways in which we can pass on parameters from my machine learning optimization that can then be used on future data to independently verify my claims about the profitability of the algorithms. In fact, I welcome friends to independently verify the model before sponsoring any capital for the new company.
Risks Associated with the Model.
There are much smaller risks associated with our fast and, short term trading style as compared to plain vanilla buy and hold strategies. When we make quick trades, it also becomes easier to limit losses since risk management techniques can be used when model loses money in small successive trades before the accrued losses could become large to make a significant decline in previously earned profits. Our trading style results in very high Sharpe Ratio for most of the financial assets we used in historical study. Our trading style is relatively safer as compared to existing trading strategies used to make profits in financial markets. While trading stocks, we do not intend to use any leverage which would make risk management of our models much easier. Before trading financial assets, we strongly vet that there are little known risks of large unexpected moves in the target stock. Our vetting stocks for large unexpected moves, our short-term trading style and zero leverage ensures that risk management of the trading portfolio remains relatively simple and straightforward process.
Prospective Financial Markets for Trading.
We want to use our model to trade HK stocks, Nasdaq Stocks, Stock exchange Index futures, currencies and commodities like oil and gold futures.
Limitations of the Model.
We have tried our models extensively on historical data but we have not done live trading with our models. We want to train our models comprehensively on paper trading with some good brokers and then use them for live trading in the final step.
Capital Requirements.
We will require initial amount of roughly two hundred thousand dollars. We need money for office space, a few fast computers, and high-speed internet. We will also need to hire about a staff of three to four mathematicians and programmers. We will also need infrastructure to secure the property.
As the profitability of our models becomes evident, we will continue to scale the trading capital with time once our market trading strategies continue to generate large profits. As we demonstrate that our trading strategies remain profitable with time, we will gradually increase the trading capital to several hundred million dollars.
About the Author.
Author is a mathematician who has made several important discoveries in mathematics.
For instance, millions of computers in tens of thousands of financial institutions use Monte Carlo simulations of stochastic differential equations every day to price derivatives and project risk of financial portfolios. Author discovered for the first time how to do higher order accurate Monte Carlo simulations of stochastic differential equations (SDEs).
Fokker-Planck partial differential equation is among ten major equations of mathematics and describes the time evolution of probability densities associated with SDEs. For the first time, author discovered several analytic ways to solve the Fokker-Planck equation.
Author has found analytic series solutions to first order ODEs, nth order ODEs and systems of ODEs. The series solution presents the true series of the analytic solution of the ODE. The method applies to all ODEs unless the ODE is singular when it has to be transformed into a non-singular form.
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amin
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| Total Posts: 309 |
| Joined: Aug 2005 |
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For Somebody who thinks that I have faked information about my research at the end of the post, I want to tell them that I did all this research over past six years and posted on this forum
https://forum.W****tt.com/viewtopic.php?f=4&t=99702
https://forum.W****tt.com/viewtopic.php?f=4&t=99702
starred letters are ilmo
It is another famous financial engineering forum. |
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amin
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| Total Posts: 309 |
| Joined: Aug 2005 |
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This is so ridiculous that instead of promoting business and exchange, linkedin is destroying business.
I had written emails to 5-6 smart connections in Hong Kong and CIA is threatened that I would take cutting edge knowledge and algorithms to China or HK and it will hurt American interests. So they asked linkedin and my public profile has been deleted and my article post about my algorithms has been removed.
They want my Chinese friends and connections to think that I am some sort of fake and therefore linkedin has deleted my public account.
I am a free human being and I suppose I am free to go where-ever I feel like.
I made the following email to LinkedIn customer support.
Re: LinkedIn Account Recovery Appeal [Case: 221118-017263]
From: Ahsan Amin (anan2999yahoocom)
To: linkedin_supportcslinkedin.com; ahsanamin2999gmailcom; anan2999yahoocom
Date: Saturday, 19 November 2022 at 11:57 GMT+5
Hi,
This morning I noticed that my linkedin account has been suspended, my public profile has been deleted and my article posts have been removed from linkedin. I never believe how linkedin can block my account without contacting me. If I have done anything wrong in somebody's opinion, you have to give me an opportunity to present my point of view before arbitrarily blocking my account and deleting my public profile.
Just yesterday, I contacted 6-7 of my Chinese connections for a partnership with my firm. This is a very significant step in my life and career. Since LinkedIn arbitrarily suspended my account, and blocked my profile, all of my Chinese friends would be thinking that I am a fake and therefore LinkedIn has blocked my account. I want Linkedin to restore my account as early as possible and also want an apology from LinkedIn.
Kind regards,
Ahsan Amin |
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| I suggest that you follow up with LinkedIn's customer support team and provide any necessary documentation to support your appeal. You may also want to review LinkedIn's terms of service to ensure that you have not violated any of their policies. Best of luck with your appeal. tunnel rush |
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