|All Time High:|
|Market Cap: |
|The price of #NMR today is $15.25 USD.|
The lowest NMR price for this period was $0, the highest was $15.25, and the exact current price of one NMR crypto coin is $15.24605.
The all-time high NMR coin price was $94.24.
Use our custom price calculator to see the hypothetical price of NMR with market cap of SOL or other crypto coins.
|The code for Numeraire is #NMR. |
Numeraire is 5.3 years old.
|The current market capitalization for Numeraire is $92,516,089.|
Numeraire is ranked #220 out of all coins, by market cap (and other factors).
|There is a big daily trading volume on #NMR.|
Today's 24-hour trading volume across all exchanges for Numeraire is $4,399,590.
|The circulating supply of NMR is 6,068,199 coins, which is 55% of the maximum coin supply.|
A highlight of Numeraire is it's limited supply of coins, as this tends to support higher prices due to supply and demand in the market.
Alien Stock Market Intelligence — Numerai’s True Contribution
Alien Stock Market Intelligence — Numerai’s True Contribution Numerai has released a new signal evaluation method called True Contribution. True Contribution is computed by treating Numerai as an end-to-end artificial intelligence system. By computing the gradient of optimized portfolio returns with respect to the NMR staked on a signal using differentiable convex optimization layers, Numerai can now surface and incentivize signals making the largest intelligence contributions to our hedge fund. Numerai’s performance is already good (our Sharpe Ratio* is 2.54 over the last 12 months) but True Contribution has the potential to make Numerai the first “Type IV” hedge fund.The Kardashev Scale The Kardashev scale proposed by Soviet astrophysicist Nikolai Kardashev is a way to grade the technological advancement civilizations based on the energy they harness. A Type I civilization can harness all energy on its planet. A Type II can harness all the energy in its solar system whereas a Type III civilization can harness all energy in its galaxy. The Kardashev scale contains no practical blueprint for creating these civilizations but it does make for fertile ground for thought experiments in considering the technologies the different types of civilizations would require. For example, in order to become a Type II civilization we would have to invent something like a Dyson Sphere to capture all the energy from our sun. In order to become a Type III civilization, we would require the technology for self-replicating robots in order to span the galaxy.A Kardashev Scale For Hedge Funds So is there a Kardashev scale for hedge funds? What would meaningfully separate different levels of technological advancement in hedge funds? We can think of every hedge fund as trading some signal of stock market predictions which should be correlated with future returns in stocks. If the signal is predictive of future returns, the best stocks to buy in a signal would have the highest signal values and the best stocks to short would have the lowest. For quantitative hedge funds, their signal comes directly out of their mathematical models. I want to propose the following Kardashev Scale for hedge funds which grades hedge funds based on the quality of their signal:A Type I hedge fund has a signal which is predictive of subsequent returns before trading costs.A Type II hedge fund has a signal which is predictive of subsequent returns after trading costs.A Type III hedge fund has a signal which can be used to make profitable alterations to the best hedge fund in the world’s signal.A Type IV hedge fund has a signal which is so good that no other signal can be used to make profitable alterations to it. A Type I hedge fund has the ability to predict future price movements; however, those predictions cannot be taken advantage of profitably because trading costs such as market impact costs would eliminate the gains. Type II hedge funds can make money after costs and therefore violate the efficient-market hypothesis. Type III is where things get interesting so let me explain a Type III hedge fund with an example. Suppose we could identify the best hedge fund in the world and let’s just say it is Renaissance Technologies. Suppose in some alternate universe, the CEO of Two Sigma (another hedge fund) decided the only thing he wanted to do was improve Renaissance’s signal. He decides to give Renaissance Two Sigma’s best signal — their highest quality stock predictions. He uploads Two Sigmas best signal to an FTP server that Renaissance can now see and use. Now, if Renaissance decides their own signal is so good already that there is no way to use Two Sigma’s signal to make profitable alterations to their trades, then Two Sigma is not a Type III hedge fund. But if there is a way for Renaissance to use Two Sigma’s signal to make profitable alterations to their trades, then Two Sigma is a Type III hedge fund. Even if Renaissance still makes 96% of the same trades they would normally make without Two Sigma’s signal, the 4% of profitable trade alterations which improve Renaissance’s signal would be enough to make Two Sigma a Type III hedge fund. Being a Type III hedge fund is a lot harder than being a Type II hedge fund because a Type II hedge fund only has to make profitable trades in the market whereas a Type III hedge fund has to make profitable alterations to a signal that is already making very profitable trades in the market. A Type IV hedge fund has a signal which no one can make profitable alterations to. A Type IV hedge fund wouldn’t just be the best hedge fund in the world, it would be a hedge fund where no other known signals anywhere in the universe could be combined to its signal to improve it. (In the example above, Renaissance would not be a Type IV hedge fund if Two Sigma is a Type III hedge fund under Renaissance trade alterations.) A Type IV hedge fund does not necessarily trade a perfect signal with perfect stock market prediction accuracy, it just means the signal it does trade is maximally good for all that is currently known. It has integrated all known signals perfectly. A Type IV hedge fund would be like an alien super intelligence for the stock market. It would be a bit like the best possible version of DeepMind’s AlphaZero playing Go where no alterations to its game by humans (or older versions of AlphaZero or AlphaGo) could improve it. I don’t think a Type IV hedge fund exists yet. But I’m the founder of a new kind of hedge fund called Numerai and I’ve spent the last few years pursuing a thought experiment: what are the necessary properties of a Type IV hedge fund and can it be built?Necessary Properties Of A Type IV Hedge Fund Clearly Type IV hedge fund would need to be able to rapidly and automatically onboard any new signal which is keeping them from being a Type IV hedge fund. If there is a Type IV hedge fund, it would be the best hedge fund therefore there cannot be any Type III hedge funds whose signals are not assimilated by the Type IV hedge fund. A Type IV hedge fund must be able to assimilate any Type III signals instantaneously or they would not be a Type IV hedge fund. Because of this, a Type IV hedge fund would need to be an open system where new signals could be uploaded by anyone to make all the alterations to trades that they are able to profitably make. Renaissance, Two Sigma or any other hedge fund for that matter have closed pre-internet, pre-blockchain organizational designs which cannot create a Type IV hedge fund just like Citibank’s organizational design could not create Bitcoin. A Type IV hedge fund would be quite a new kind of thing indeed. It might look and feel a bit more like Bitcoin than Two Sigma. Of course, an obvious property of a Type IV hedge fund is that it would need to be rich enough to offer enough money to buy up all the Type III signals. If Two Sigma can make profitable alterations to a candidate Type IV hedge fund’s trades, then that Type IV hedge fund needs to have the capital to incentivize Two Sigma to shut down its trading business altogether and instead sell their signals to the Type IV hedge fund. Getting Two Sigma to turn over their signal would be very expensive indeed. Perhaps even Renaissance couldn’t afford to become a Type IV hedge fund even if they really wanted to be one. A Type IV hedge fund would have to be an open market place for buying signals. Numerai is such a system already. Anyone can submit signals to Numerai using our free obfuscated data or Numerai Signals using their own data. Numerai doesn’t have that much money but we do have about $150m in our own cryptocurrency, NMR, which makes Numerai already by far the highest paying data science competition in the world and the largest buyer of stock market signals on the internet. With an open way to onboard signals and cryptocurrency to incentivize people to submit new signals, it may appear as though Numerai has the right properties to become a Type IV hedge fund.A Hidden Assumption But there is a lingering, pernicious assumption in this line of argument which is hiding from view. It’s the kind of assumption that is missed in thought experiments but shows up in practical reality. And here it is: that Numerai would have any idea how to evaluate whether some new signal will improve our existing good signal. Numerai already combines thousands of signals submitted by our data scientists into what we call the Meta Model. So given a new signal, how do we know we can include it in this already large ensemble of signals and have that inclusion generate profitable alterations to the trading strategy? Without a good technological solution to this signal evaluation question it is impossible to know whether a signal is of Type III and therefore reaching Type IV is impossible. Over the years at Numerai, we have had to learn how to get good at signal evaluation. We have made many improvements to signal evaluation since Numerai was founded. For example, we launched MMC and staking where Numerai data scientists stake NMR on their models to prove they believe their models will work (generalize) out of sample. Both MMC and staking improved the quality of signals on Numerai. But we have never solved the signal evaluation problem. We could not determine whether in our end to end system whether a signal was Type III with respect to our existing Meta Model. But today we are announcing a new system we have spent years building towards called True Contribution, which solves the signal evaluation problem.Signal Evaluation With Information Coefficient It seems natural to assume that signals with high correlation with future stock returns are likely to be the most helpful in Numerai’s Meta Model. Because of this, Numerai has spent many years making payouts to data scientists based on how good they are at generating signals with strong correlation with Numerai’s targets (which are like residual returns). But this incentivizes Type I signals, and rewarding signals based on their correlation with targets isn’t an accurate representation of how much that signal truly contributed to Numerai’s post-optimization portfolio return. Rewarding users based on their correlation with targets simply ignores too many very important details such as: the correlation of the signal with other signals, its interaction effects with the existing Meta Model or the hundreds of portfolio optimization parameters that Numerai uses to turn the Meta Model signal into a balanced portfolio of a few hundred stocks.signal evaluation based on correlation with a return target is inadequate Clearly we need a signal evaluation method which accounts for every detail in the system to be able to evaluate whether a signal can make profitable alterations to Numerai.End-To-End Signal Evaluation To be end-to-end we need to map out every aspect of how a signal and the NMR stake associated with that signal affects the final portfolios constructed by Numerai’s optimizer.how Numerai turns signals into optimized portfolios As you can see in the diagram above, Numerai first combines the signals generated by data scientists’ machine learning models. We do this by computing the stake-weighted average of every signal to create the Stake-Weighted Meta Model. A data scientist who stakes a large amount of NMR on their model will have a larger weight in the Stake-Weighted Meta Model. The Stake-Weighted Meta Model is still just a signal of predictions on ~5000 global equities. It still needs to be turned into a realistic portfolio with hundreds of risk constraints (such as market, country and sector risk neutralization), and that’s what the optimization step does. Once the optimizer creates a realistic hypothetical portfolio that satisfies all risk constraints, Numerai can observe the subsequent returns of the portfolio. To evaluate a signal properly, we must consider the signal’s effect on the whole system above from signal to Stake-Weighted Meta Model to returns of a post-optimization portfolio. And that’s what True Contribution does.True Contribution Put simply, True Contribution is the answer to the question: if a data scientist staked slightly more on their model (thereby increasing their weight in the Stake-Weighted Meta Model), what would the change be to post-optimization portfolio returns? To someone in quantitative finance, you could think of True Contribution as a sophisticated signal attribution. To someone in machine learning, the diagram above of how Numerai works may look reminscent of a neural network architecture. And if you’ve ever built neural networks, you might be wondering if it is possible to take the gradient of the optimized portfolio return with respect to the stake. That is exactly what True Contribution is. But how is the gradient computed through the portfolio optimization layer? As it turns out, this is possible using new techniques developed by Stephen Boyd of Stanford University, Brandon Amos from Facebook AI et al (see their paper: Differentiable Convex Optimization Layers) By using cvxpylayers, we can include a cvxpy defined convex portfolio optimization as a layer in a PyTorch model. This lets us efficiently compute the gradient of the optimized portfolio return with respect to the stake values and determine the True Contribution of every signal submitted to Numerai.True Contribution evaluates a signal end-to-endConsequences of True Contribution With True Contribution portfolio construction, size of stake, originality of the model and the strength of the signal are all taken into account in the exact proportions in which they actually matter for producing returns in realistic portfolios that Numerai could actually trade. Signals which are original and help Numerai to make different, and more profitable trades to what we otherwise would have will now receive the highest NMR rewards on their stakes, and will therefore tend to have larger and larger weights in the Stake-Weighted Meta Model. This reward feedback is important to every data scientist on Numerai too because they can now improve their models to maximize their True Contribution. With True Contribution, Numerai is creating a feedback loop designed to continuously incentivize the creation and submission of signals which make profitable alterations to our hedge fund and disincentivize all other signals. Every round of Numerai will become like another pass of backpropagation on the overall cybernetic system of Numerai. Feedback and error correction propagate through a layer of distributed AI models, a blockchain staking layer, a Meta Model, and a convex optimization. In other words, with every round of Numerai we take a step closer towards becoming a Type IV hedge fund.Early Indications of True Contribution Staking on True Contribution will begin on April 9th but in the meantime we have backfilled True Contribution for every user on Numerai for the last ~2 years. The results show large potential for True Contribution as a new signal evaluation metric on Numerai. For example, there are many data scientists such as LANCE_A_LOT with very high True Contribution ranks but much lower ranks on other metrics such as correlation with the target. It’s clear that at least over the recent period, LANCE_A_LOT had a model that was helping Numerai the most but they were not rewarded properly for their contribution.LANCE_A_LOT currently has the highest True Contribution (TC) but is ranked low on other metrics HB is an engineer at NASA Jet Propulsion Lab working on the Europa Clipper Mission, and a long time Numerai data scientist. HB has multiple models submitted to Numerai but the model he is staking the most NMR on (765 NMR worth $22,000) has much lower True Contribution than his other models. Some of his best models in terms of True Contribution have no stake on them at all meaning these excellent models have zero weight in the Meta Model and are not being rewarded at all.HB’s highest staked model has low True Contribution and his model with high True Contribution With the ability to start staking on True Contribution, data scientists will begin moving their stakes to models with the highest expected True Contribution. In this dynamical system, as data scientists like HB and LANCE_A_LOT adjust their stakes to earn more True Contribution, there is no reason to believe Numerai’s Stake-Weighted Meta Model could not become significantly more intelligent.Better With Time Of course, we don’t know whether we will reach Type IV. But what type of things would you expect to see if we were on the path to become Type IV? I think the key thing to look for is risk adjusted performance increasing with time and AUM. Hedge funds tend to get worse with time. How sad. This happens because of signal decay (the trading signal a hedge fund started with gets worse with time as the market gets more efficient) and capacity constraints (the trading strategy that worked great on $10m doesn’t work at all on $100m). But Numerai so far isn’t getting worse with time at all — it’s getting better, and that’s a very good sign.Numerai’s 12 month trailing Sharpe Ratio* (currently 2.54) Over the period in the graph, Numerai’s hedge fund AUM has almost 10xed from about $7m to $64m (still early). The decay on our signal over time and the AUM growth should hurt performance over time but our risk adjusted returns (Sharpe Ratio) continue to increase with time. And that’s because over this same period, Numerai has gone from 300 staked models to over 4000 staked models in the Meta Model. Numerai does not have signal decay, we have constant signal rejuvenation — the Meta Model is rebuilt with the latest signals every week. What’s remarkable about this continued increase in our risk adjusted Sharpe happened without Numerai users receiving the correct feedback on their models i.e. without True Contribution…credit: wait but why’s The AI Revolution: The Road To SuperintelligenceLearn more about the technical details of True Contribution from Numerai’s Chief Scientist in this post on the Numerai forum.Join us at NumerCon, our conference in San Francisco on April 1st 2022.Improve the Meta Model by submitting to Numerai and Numerai Signals.Learn more about Numerai’s hedge fund. Thank you to the geniuses at Midjourney for the AI-generated cover art. (the prompt here was: “purple cybernetic Dyson sphere on Wall Street”) *Sharpe Ratio calculations are based on returns gross of fees and assume a risk-free rate of 0%. The Sharpe Ratio information provided is historical and is not a guide to future performance. Investors should be aware that a loss of investment is possible. No representation is being made that any investor will or is likely to achieve profits or losses similar to those shown. For further information see: https://numerai.fund Alien Stock Market Intelligence — Numerai’s True Contribution was originally published in Numerai on Medium, where people are continuing the conversation by highlighting and responding to this story.
Numerai Outperforms by 26%, Raises Up To $150m
Numerai Outperforms Market Neutral Hedge Funds by 26%, Raises Up To $150m *Numerai is a quant hedge fund built by thousands of data scientists around the world. We have quietly been beating the most prestigious hedge funds in the industry but have never published our performance. Today, we’re excited to share the performance of our hedge fund for the first time with the data scientist community who built it. **This blog post was updated 2/3/2022 to reflect Numerai performance through 12/31/21 as well as to update the fee methodology used in calculating net performance.What is Numerai’s performance? We started the current version of Numerai’s hedge fund in September 2019. Around this time we changed the dataset, the optimizer, leverage and the way Numerai’s tournament works (see Achieving Meta Model Supremacy). This was an unbelievably challenging time to start building a track record for a quantitative hedge fund. Within a few months, Covid-19 caused massive volatility in global markets and led the worst period on record for many quant hedge funds including Renaissance’s RIDA fund which was down -32% by the end of 2020.Quant hedge fund performance in 2020 (source: Bloomberg & Aurum) Yet Numerai’s market neutral hedge fund performed unusually well. Although 2020 was challenging, it allowed Numerai to differentiate itself from the blue chip quant hedge funds. It highlighted how risk exposed some of these older funds are. Many of these funds take large factor exposures which Numerai’s fund avoids.Source: Aurum, AQR Since strategy launch in September 2019, Numerai is beating Aurum’s Quant Equity Market Neutral Index by 26.46% and AQR’s Market Neutral Fund by 29.61%***. Not bad for the last two years. The correlation between Numerai’s return stream and AQR’s is also an extremely low 0.01 demonstrating Numerai really is doing something completely different to traditional quant.Why is your return so low? Almost everything is up 50% or more from Apple to Bitcoin to Tesla. Market neutral hedge funds are unlike any other investment product. Most funds have an agreement with investors to take on risks (their “mandate”). A venture capitalist has a mandate to risk the money on technology startups. Cathie Wood has a mandate to risk the money on innovation stocks. A market neutral fund on the other hand has a mandate with the investor to be neutral to stock market risk i.e. to hold portfolios which do not contain that risk. It is this mandate to run the portfolio with very low risk which can lead to much lower return than the market when it is going up but much higher relative returns in market crashes.How can a fund which trades stocks be neutral to stock market risk? The key idea with a market neutral fund is to be short $100 of stocks for every $100 of stocks the fund buys. If this is maintained, the net effect is that the hedge fund has ~$0 of market risk. Roughly this is what is meant by a portfolio having a beta of zero which is what market neutral funds run. Of course, many portfolios of stocks which have a beta of 0 can still be risky. For example, if a hedge fund bought $100 of American stocks and went short $100 of Japanese stocks then that portfolio would be very exposed to country risk. (In particular, the risk of American stocks dramatically underperforming Japanese stocks). Buying $100 of Apple and shorting $100 of Microsoft might be a safer portfolio because at least both stocks are in the same country and the same sector. This portfolio of long Apple and short Microsoft would be considered country and sector neutral. A hedge fund like Numerai is trying to stay neutral to things like the market, country, sector, and hundreds of other risk factors. The more risks a portfolio is neutral to, the lower the volatility of the portfolio and the more we believe that the risk of using leverage is reduced. So yes, it’s confusing to read about a hedge fund with 50 PhDs that only earned 5% in year where everything went up but when you consider how little risk the hedge fund took and how low the volatility was, 5% can be impressive. 5% is a lot more yield than many corporate bonds pay and maybe the risk and volatility profile of the hedge fund is lower than bonds too.What if you ignored the shorts and leverage in Numerai’s portfolio and you only bought stocks? How would that do? Good question. Very well. If a hedge fund can earn money with a beta of 0 (where we have to go short something every time we go long something), that almost always means the stocks we went long would beat the market. One of the reasons Numerai started with a market neutral fund is exactly because it’s the hardest game on Wall Street. It’s hard because the only way to make money is by being good at selecting stocks and managing risk. There is no free money to earn from taking market risk because of the market neutral mandate. So we figured Numerai data scientists can win on the Hard Mode of Wall Street then we can build any kind of fund in the future after that. It took many years but we’re now getting really good at Hard Mode and because Numerai is growing so fast, we’re getting better at an accelerating rate which is very rare for a hedge fund business model. So here’s what Numerai’s performance would look like if you ignored all the shorts and just built a portfolio out of our long positions.Numerai US Long Only vs Russell 20009/9/2019–10/01/2021 Numerai US Long Only: +98.25% iShares Russell 2000: +51.93% Numerai Outperformance: +46.32% Numerai’s US Long only portfolio earned 98.25% vs the Russell 2000’s 51.93%. It’s important to note that this isn’t a backtest or simulation. These are the stocks Numerai actually bought at the price we actually paid for them according to our audited track record. Of course, the fund above isn’t a real fund (our real fund has lots of offsetting shorts and leverage) but this is just an illustration of how Numerai’s US longs faired against a stock market benchmark which most people are familiar with. We chose the Russell 2000 instead of the S&P 500 because the Russell 2000 has smaller stocks which match Numerai’s US long holdings better. (We beat the S&P 500 too fwiw which was “only” up about +43% over the period vs Numerai’s 98.25%.) None of this is definitive evidence that Numerai’s fund performance is driven by skill and not luck but it is suggestive. So is this:Daily relative return of Numerai’s US Long Only vs the Russell 2000 If a fund is lucky, it typically has long periods of outperformance and long periods of underperformance due to its risk exposures. Here vs the Russell 2000, Numerai appears to have a strong and consistent relative outperformance which gets stronger as the number of data scientists staking models on Numerai increases. Numerai beats the index on 54.36% of trading days which is quite hard to do by luck across 539 trading days. The tracking error of the portfolio is 7% (for non-quants this is a measure of how much the portfolio moves with the benchmark). This is indicative of the risk of the portfolio vs the benchmark and 7% which, in our opinion, is quite low. The information ratio is 2.08 (for the non-quants, this is a bit like the Sharpe ratio of the return above the benchmark). These are really very good numbers. I think they would put Numerai near the top of the category of US long only funds. There are not many funds with an information ratio of 2.08 (0.5 is considered a good information ratio). So congratulations to the Numerai data scientists around the world whose models created this excellent track record.If the long only version of Numerai would make 98.25% over two years why do the market neutral hedge fund? Diversification. The S&P does not usually do what it’s recently been doing. Sometimes markets crash and stay down for a long time. From 2000 to 2010 you would have made almost no money holding stocks in the US. Another decade like that is in our future and when it comes it will be wise to be holding a few things which can survive and thrive such as market neutral hedge funds. Even if an institutional investor expects Bitcoin to outperform the stock market, it doesn’t mean they should put all their money into Bitcoin or even most of it. Institutional investors care about things like volatility adjusted return because they are in a multi-decade game where they will live to see multiple environments. By being uncorrelated with the market and many more risks besides, market neutral funds will always have a place in a sophisticated investor’s portfolio. They may even be the perfect diversifier in world with extreme valuations in every risk asset. Also, any institutional investor can buy equity exposure or tech exposure or Bitcoin exposure for almost nothing. These are just risks so they are cheap to create and manage. It’s hard to create a market neutral fund and therefore hard to replicate one for cheap. Institutional investors are often happy to pay fees to a market neutral hedge fund like Numerai for a unique, uncorrelated diversifying exposure that they can’t get anywhere else.What’s Numerai’s AUM? We recently raised money from a new investor. They are Numerai’s first big institutional investor, a pension fund. They have a deal with Numerai where they gave us $20m but can invest up to $130m more later. Subsequent investments after the first $20m are at lower fees so they are incentivized to use their $130m in capacity rights to re-invest as new investors come on board. We are only at about $43m in AUM right now so we’re still very small for a hedge fund. But Numerai’s fund trades with 5.5x leverage so almost one quarter of a billion dollars is controlled by Numerai’s data scientists right now. Pretty cool. Raising assets into a quant fund is only a good idea when the fund has achieved some significant milestones. For Numerai, we wanted to see four things before raising assets:Numerai’s fund is outperforming its peersNumerai’s fund is meeting its backtest expectationsNumerai’s fund can demonstrate risk management in a large macroeconomic stress testThe fund returns are attributed to skill (stock selection) not luck (risk exposure). It took a while but today we’re at 4/4:Numerai is beating our most comparable index Aurum Quant EMN by 26.46%, and AQR by 29.61%*** over two years.We observe live return and volatility in line with our backtest expectations.We handled the Covid-19 market volatility extremely well going down 1.54% in March 2020 when the S&P fell as much as 28%. We handled the momentum crash in November 2020 with minimal drawdown. We handled the meme stock rally in January 2021 with minimal drawdown.Over 80% of the Numerai’s fund return is due to stock selection, meaning <20% of the return can be attributed to risk exposures such as factor, country or sector exposure. Learn more or contact us on numerai.fund https://medium.com/media/0d1de3f40d46e64d656f3ed6579e2610/hrefQuant hedge fund performance in 2020 (source: Bloomberg & Aurum)*Numerai Master Fund One, Ltd., thereinafter referred to as “Numerai” **This blog post was updated 2/3/2021 to reflect Numerai performance through 12/31/21 as well as to update the fee methodology used in calculating Net Performance ***AQR compounded return for the period reference monthly returns from Class N — QMNIX. [†]: Numerai Performance is reported net of fees using Founders Class structure of: 1% Management fee and 20% Performance feePerformance results of Numerai’s hedge fund is presented for information purposes only. Numerai’s fund performance result was calculated net of management fee and incentive allocation, assuming a management fee at the rate of 1/12 of 1% (1% annually) of beginning net assets each month paid in advance and an annual incentive allocation calculated at a rate of 20% of trading profits. Rate of return is calculated by dividing net performance by beginning net assets. Performance of individual investors may vary based upon differing management fee and incentive allocation arrangements, and the timing of contributions and withdrawals. Returns are inclusive of the reinvestment of dividends and other earnings, including income from new issues. Returns may vary for investors who are restricted from participating in new issues. Performance results have been reviewed and audited by an independent accountant. The information provided is historical and is not a guide to future performance. Investors should be aware that a loss of investment is possible. No representation is being made that any investor will or is likely to achieve profits or losses similar to those shown.Aurum Quant Equity Market Neutral contains information published by Aurum Research Limited of hedge funds that have been assigned a “Quantitative Equity Market Neutral” strategy. Such information has not been audited and is provided on an “as is” basis.Numerai’s hedge fund is very unlikely to be a suitable investment for individual investors due to the tax treatment of short term capital gains. Numerai’s hedge fund is designed primarily for institutional investors like endowments and pension funds.Numerai’s crypto token NMR is a staking token used on Numerai by Numerai’s data scientists. NMR is a volatile cryptocurrency which moves up an down with other cryptocurrencies based on supply and demand. Numerai Outperforms by 26%, Raises Up To $150m was originally published in Numerai on Medium, where people are continuing the conversation by highlighting and responding to this story.
Building The Last Hedge Fund — Introducing Numerai Signals
Building The Last Hedge Fund — Introducing Numerai Signals Numerai is now the first hedge fund to source original stock market signals, built from any dataset, from anyone in the world. Numerai has allocated $50 million in cryptocurrency rewards for the most original signals. Long before artificial intelligence destroys the world, narrow artificial intelligence will eat the stock market. At Numerai, we’ve always felt it would be hard to build that artificial intelligence ourselves. Instead we’ve built a system for collecting stock market intelligence built by others. Three years ago, I published Numerai’s Master Plan. Phase 1 of the plan was to monopolize intelligence. And we have since built the largest stock market data science tournament in the world by providing free data and paying out millions to our community — over $40 million since launch. In phase 1, Numerai’s data scientists could use any machine learning algorithm to model the data we provided to create trading signals for our hedge fund. But for Numerai to contain all stock market intelligence, we need to accept signals generated from any dataset. Today, we’re announcing a new system that we’ve been working on for a long time. It’s called Numerai Signals and it lets Numerai source and reward stock market signals built with any model, trained on any dataset. It is the first step in phase 2 of the master plan: monopolize data. https://medium.com/media/e1bcc9a06ba065ebde070f220c323459/hrefWhere Is The Next Ken Griffin? We have stock markets because it is valuable to society for stock prices to be right, to reflect all the information they can. If the prices are right, the best companies are getting the most capital to solve the problems in the world. It is really quite true that Tesla could not hire people, could not access capital, could not manufacture electric cars if its stock price was stuck at $1 and no one stepped in to trade it up to a fair price. In 1987, the dream of people stepping up to correct market prices was alive. In his Harvard college dorm room, Ken Griffin raised $265,000 from his grandmother and his dentist so he could start trading stocks. He convinced Harvard to let him install a satellite dish on the roof of Cabot House to receive stock quotes. At 19 years old, Ken Griffin’s insights were reaching and moving markets — in small ways at first and then in larger and larger ways as he proved himself. In 2020, the dream is dead. Since 1987, many things have gotten better. Nearly every university student in the world has wireless internet that’s one million times faster than Ken Griffin’s 1987 satellite dish. And they have Robinhood on their smart phones that they can use to trade “for free”. On the surface, it appears as though the playing field of the stock market has never been more level. But in fact, it has never been more uneven. A bright young Ken Griffin in 2020 could get Robinhood but would soon realize that he’s going up against the real Ken Griffin who is still going strong as founder and CEO of Citadel: which manages $32 billion, has tons of data, and 1400 employees who are incentivized not to leave a scrap of money on the table for any of the young Ken Griffins. Not only that, but the real Ken Griffin is also buying up Robinhood’s trade flow. For a young Ken Griffin to have any hope at all he’d need to trade with a prime broker where he would need $10m of capital just to get started. To have any outside investors, he’d need a fund administrator, lawyers, auditors, etc. In 2020, the costs are just too prohibitive for starting a new hedge fund. Pushed out of the real stock market with its high barriers to entry, little wonder the young Ken Griffins of 2020 would sooner start crypto funds than real hedge funds. At least in the crypto markets they could more credibly have an edge and all the costs would be lower. But that’s not good. Having well-priced Dogecoins has a lot less societal value than having well-priced Tesla stock. The magic of markets is in the consequence of all the trading activity: in getting capital to the right companies. If all the young Ken Griffins of our generation become Dogecoin degenerates, the consequence is merely more Dogecoin memes. But unfortunately, the high barrier to entry in starting a hedge fund is not the only problem. There’s a big problem in how the stock market gives feedback as well.A Feedback Problem Leads To Waste One of the strangest things about working in quantitative finance is not being able to see the state of the art. If you’re a car maker, you can test drive your competitors’ cars or even buy one and dismantle it, and you can see exactly how your car compares. As a car maker, you can claim and prove definitive things like “our car has a top speed higher than the Porsche 911”. Unfortunately, quantitative investment strategies are not like this at all. If you’re a quant, you’re in the dark about your competitors. You have no idea what they’re doing. You have no idea if you’re good. You might develop a trading strategy that backtests well or seems to work in live trading but you have no idea whether what you have built is unique or better than the hedge fund across the street — and they definitely won’t tell you. Not knowing the state of the art is an infuriating and wasteful situation for the hedge fund industry. A startup hedge fund today might raise capital, hire employees and try to build a business around a trading signal that Citadel discovered in 2009 and Renaissance Technologies discovered in 1989 — and they’d never know it. And if a signal has been discovered by others already, the extent to which the signal continues to work is only the extent to which Renaissance and Citadel are letting it work. A new hedge fund rediscovering a signal that is already known cannot possibly have a defensible or durable edge. In the long run, the market can’t pay everyone for the same discovery, it can only pay participants who provide non-redudant signals to the market. Unfortunately, for thousands of hedge funds, most of their signals are of the redundant kind: worthless because they’re already discovered. Trading against another fund when you have a strict subset of their signals is like choosing to play chess against Magnus Carlson where you start without a queen. But many new hedge funds try to do just that. Convinced by often unscientific backtests, a continuous stream of new hedge funds without any differentiated signal end up food for Renaissance’s monster because they had no way to know Renaissance had their signal already, and had them beat long before they even started trading. So we have two major problems:It’s too expensive to start new hedge funds so good signals are stuck on the sidelines.Even if you have started a hedge fund there’s no way to know whether you have discovered a signal that has not already been discovered — no way to know whether you’re providing a signal that’s original and valuable to the market. A bleak situation for the young Ken Griffins indeed. So bleak it almost makes you want to trade Dogecoin. Not so fast.The United States Signals Registry Let’s imagine a hypothetical new world without these problems. President Trump wants to take his mind off of COVID-19 and do something edgy before the election. He decrees that starting October 31st 2020 all US based hedge funds will have to submit their data to the United States Signals Registry (“Trump’s USSR”). Every signal that every hedge fund uses for trading must be provided as a live feed to the USSR. All Renaissance’s signals would need to be there, all Two Sigma’s, all Citadel’s, all George Soros’. Trump says it’s going to be huge. Trump wants all the signals so that the government can better understand the stock market and interfere with it more sensibly. But Trump’s vision for the United States Signals Registry has hedge fund managers up in arms. They don’t want to share their data with anybody, but Trump assures them that the data will be kept secret and that compliance is their only option. In New York city, Jim Simons, the founder of Renaissance has an idea. He knows Trump is going to get his signals registry but Simons is not one to give up something for nothing in return. Jim Simons offers Trump an amendment to the way the USSR works: whenever anyone submits a signal to the USSR, they should get feedback on whether their signal is different from all the other signals in the system. Trump doesn’t sweat the small stuff and immediately agrees with Simons’ amendment. The USSR is signed into law and established. Every hedge fund starts submitting their signals, and the USSR provides feedback on the extent to which a signal is different from all the other hedge funds’ signals. A few days after the launch of USSR, something powerful begins to happen in the stock market. Because market participants can now know before trading a signal whether or not another hedge fund has already discovered it, they stop wasting time on unoriginal signals. A golden age begins. Startup hedge funds clamor to upload new signals to the USSR to find out which of their signals are actually original. They focus on finding non-redudant signals — signals that no other hedge fund has found that could not possibly be integrated into stock market prices yet because they know that these original signals are the only durable edge they could have. With market participants no longer in the dark about the other signals out there, a gold rush toward a new kind of market efficiency ensues. Quants begin to focus exclusively on new discoveries, integrating new types of data into their signal totally different from anything else in the USSR. Some hedge funds are so demotivated by seeing how correlated their signal is to other signals in the USSR that they give up altogether, viscerally experiencing for the first time how little value they’ve been adding to the market their whole lives. Hedge fund investors begin to adopt a new investing policy. They refuse to invest in any new hedge funds based on backtests; they instead ask all new funds to first demonstrate that their signal is unique according to the USSR. Data providers who are trying to sell data sets to hedge funds are told by the hedge funds to first submit their data through the USSR. Hedge funds refuse to even consider new datasets without first seeing the USSR’s originality report on their signals. Because of the credibility conferred to any signal with a positive result from the USSR, many hedge funds decide to shed 100% of their compliance, fund admin, legal, execution costs and instead sell their best, most original signals to other hedge funds. It seems peculiar to young people that we ever had 10,000 hedge funds in the first place with their duplicated datasets, unoriginal signals, paper pushing fund administrators, etc. Because people trust the USSR, a new hyper-focussed industry of signal generators emerges. These signal generators only create signals and couldn’t care less about their trading implementation. The young Ken Griffins become signal generators, and their insights now reach the real stock market and bypass the current barriers to entry. Interest in trading Dogecoin among young people collapses. A few years later, Donald J. Trump and Jim Simons share the Nobel Memorial Prize in Economic Sciences for “for the establishment of the United States Signals Registry and its unreasonably effective contribution to stock market efficiency”. Although ashamed to admit it, even Hillary Clinton agrees the United States Signals Registry has led to better markets, far fewer wasted costs in the industry, and a far better world. So what does any of this have to do with Numerai Signals? Numerai Signals is the United States Signals Registry.Numerai Signals Numerai Signals is a new way to upload stock signals and figure out if your signal has predictive value after being neutralized by all the other signals Numerai already has including signals uploaded by others. What does predictive value after being neutralized by all other signals mean? Consider the following example. Suppose Numerai has the P/E ratios of all stocks already (we do). This is a classic “value” signal used by many quants. Now suppose a new Numerai Signals user starts submitting his own custom value signal. This custom value signal is created with a neural network trained on P/E, Price to Book and Price to Sales ratios. He believes his custom value signal is much better and more predictive than simple P/E ratios so he uploads it to Numerai Signals.what a signal uploaded to Numerai Signals looks like — just a csv with 2 columns Upon upload Numerai can see that his signal is very correlated with the P/E ratios we already have. In fact, even though he has put a lot of effort into his custom value signal, its correlation with standard P/E is 0.85. In lay speak, you might say Numerai already has 85% of what this new custom value signal is offering, so Numerai only cares about the performance of the 15% we don’t already have. Numerai Signals isolates the component of the signal orthogonal to P/E (and any other signals we have already) and rewards the creator of the signal based on that. By the predictive value after being neutralized by all other signals, we mean the predictive value of this orthogonal component.the value is the orthogonal component of the signal — it’s originality Numerai Signals is all about creating signals with predictive orthogonal components — the original part of the signals that we don’t already have. The incentives are therefore around creating signals from unusual data sources or unusual modeling techniques. The feedback mechanism of Numerai Signals much more realistically captures what’s valuable about a signal. Numerai Signals rewards people how the market should reward them: for the marginal predictive value of the non-redudant component of their signal. By surfacing and rewarding only this non-redundant component in the signal, Numerai Signals can have the same impact on the world as the United States Signals Registry. It can allow signal generators to replace hedge funds. It can allow hedge funds to shed the burdensome costs of being money managers and become signal generators instead.Why use Numerai Signals? In our hypothetical world when Trump proposed the United States Signals Registry, nobody wanted to share their signals. He only got the hedge funds submitting to the USSR because he said compliance was their only option. Numerai can’t force anyone to use Numerai Signals, so why would anyone choose to use Numerai Signals instead of trading their signal themselves? There are many reasons:Most signals are not profitable enough by themselves to be worth trading. (No one wants to invest in a hedge fund trading a signal that returns only 3% per year but this same signal would be very valuable to Numerai when combined with other signals).Trading a signal properly in a hedge fund structure is prohibitively expensive but submitting to Numerai Signals is free.It’s unwise to trade against other funds that may already have your signal and many more as well.When you trade on the market, you can’t tell if you’re trading an original signal. On Numerai Signals, you can see exactly how original you are.Nothing submitted to Numerai Signals is exclusive. You can trade a signal yourself and submit it to Numerai Signals as well.To earn money on Numerai Signals, you don’t need to trade anything instead you simply submit your signal and stake it with the NMR cryptocurrency just like on Numerai. The minimum stake is only 0.01 NMR ~30 US cents.On Numerai Signals you can evaluate the originality of your signal on historical data as well. These historical diagnostics give fast feedback on the originality of your signal before you stake it.Even a signal with low correlation with the target after neutralization can be very profitable on Numerai Signals. Here, a 0.0126 correlation with the target after neutralization earns 254.39% APY on the NMR staked — much more than you would earn trading it yourself. — Examples of Signals Users. — An intern at PayPal strings together some transaction data and comes up with what he believes is a strong signal that works to predict the returns of tech stocks. He approaches PayPal’s CEO and says “I think we could start a hedge fund with this”. PayPal’s CEO says, “our business is not trading; trading is a complicated, expensive business”. That evening the intern discovers Numerai Signals and sends an email to the CEO: “we can make 200% per year on my signal built from our data without doing any trading at all”. PayPal’s CEO is intrigued and agrees to let the intern try it out with a small stake. Because the intern’s signal is so good and original, PayPal decides to build a whole new business unit around submitting signals to Numerai. They earn large payouts just by submitting a small csv file to Numerai’s API each week. A New Jersey based global equity hedge fund has been banging its head against the wall trying to get their quant strategy to work. Over the last few years, performance has been weak. Last year, they made 2% but spent 8% on trading costs (short borrow costs, market impact costs, etc). If they didn’t have to pay the trading costs their signal would earn 10% per year. They consider selling their signal and technology to a hedge fund but every hedge fund they approach says “if your signal is so good, why don’t you trade it yourself”. A quant at the firm gets permission from his boss to upload the signal to Numerai Signals. He immediately discovers their signal is highly orthogonal to everything else on Numerai. Their hard work really has meant something. He tells his boss that their signal is original but they may never be able to cover their costs trading it in the real stock market. They decide their best move is to submit their signal to Numerai Signals where they pay no trading costs. For a while, they continue trading the signal as well but eventually figure out they can make a lot more money a lot easier just by submitting to Numerai Signals. Previously, their only option was to close their fund and deprive the market of the valuable signal they’d worked on for so long but now their signal gets full expression in the market through Numerai Signals. Jimmy has been a Quantopian user for several years. He has taught himself to be a quant on Quantopian. He has run 27,000 backtests, and he has even built a few signals that ran in the Quantopian hedge fund before it shut down. Despite his skills and performance on Quantopian, Jimmy has no way to get full value for his signals. He asks Quantopian permission to allow him to automatically submit his signals to Numerai Signals. Quantopian likes the idea and agrees to let their entire community submit their signals to Numerai Signals automatically through the API. Quantopian strikes a deal with Numerai to share in the upside. There is little doubt in Jason Rosenfeld’s mind that modern language models like OpenAI’s GPT-3 are the future. He’s convinced that AIs can basically comprehend text at this point. He has an idea to use language models to create new, original stock market signals. Jason Rosenfeld has been working on NLP for years and used to work at $38 billion hedge fund, Millennium but decided to start his own company, CrowdCent. Although, Jason has been modeling Numerai’s data for a while, there has been no way for him to use Natural Language Processing techniques like those used by GPT-3 on Numerai’s data (it’s numerical data, not set up for language models). But CrowdCent has gigabytes of text data referencing different stocks which Jason has already built signals on. Jason knows his signals are institutional-grade, and capable of powering trading strategies at the likes of Millenium but Jason knows he can make more money submitting his signal on Numerai Signals. This is a real example. Jason is currently in first place on the Numerai Signals leaderboard and he is currently building out a team to stay number one. If you’re like any of these examples, sign up to chat with us or read our technical documentation.All The Signals Numerai Signals is a way for Numerai to collect entirely new complementary signals based on any new data sources. It is an extension of Numerai’s API. You can sign into Numerai Signals with your Numerai account, stake with your existing NMR wallet balance, and use the same API keys to submit. Just like Numerai, it is built on the Erasure protocol and uses the Numeraire (NMR) token for staking and payouts. Even the universe of ~5000 stocks is exactly the same between Numerai and Numerai Signals. The reason Numerai Signals is so tightly coupled with Numerai is because its purpose is to gather signals which enhance Numerai’s core signal — our meta model which combines all the models staked on Numerai. Any data scientist without data can create signals based on Numerai’s data and continue to improve the meta model. But now any data provider, quant, or even other hedge funds with their own data can use Numerai Signals to see how original their signals are and stake them if they believe in them. Over time the goal is that Numerai Signals becomes the best place to monetize all stock market signals. With there finally being a way to know whether your signal is original, you can now focus your efforts on that piece alone. Instead of having 10,000 hedge funds each duplicating each others strategies in the dark, we can finally have consolidation in the industry, with one peaceful, open hedge fund connected to every signal. Numerai Signals is a major next step for Numerai’s master plan so we have decided to allocate $50 million of our cryptocurrency treasury for rewarding new signals. Compared to Two Sigma’s financial modeling competitions on Kaggle ($200k) and all Quantopian’s contests ($180k), Numerai Signals rewards are 100 times larger making us the most important quant community on the planet. Join us. I’m doing an AMA on r/numerai today. Ask me anything or tweet @richardcraib. Submit to Numerai Signals Sign up on signals.numer.ai Download example signal and example script for creating a signal Read our technical docs for more details on how Numerai Signals works Talk with us and our community — sign up to community.numer.ai Interested in working at Numerai? See our open full time positions for season 5Please read our Terms of Service for further information. Building The Last Hedge Fund — Introducing Numerai Signals was originally published in Numerai on Medium, where people are continuing the conversation by highlighting and responding to this story.
Achieving Meta Model Supremacy At Numerai
An inside look at the backtests at Numerai, and a conversation with Marcos López de Prado, Numerai’s new scientific advisor. Jump to the video presentation on YouTube.Supremacy Numerai is a hedge fund built by a network of data scientists around the world. We give away hedge fund quality data and allow anyone to model it using machine learning, and we combine the best models to create the Meta Model which manages the money in our hedge fund. But why is this a good idea and is it working? The first assumption behind Numerai’s approach is that machine learning is better than standard statistical approaches for modeling financial data. It wouldn’t make sense to sign up thousands of data scientists around the world if building a hedge fund was just a matter of collecting data and running linear regressions. It may seem obvious that modern machine learning models would be better than classical models but even though machine learning algorithms dominate on almost every data modeling problem, their performance on financial data is still in dispute by many academics and industry professionals. Of course, it’s impossible to prove that machine learning models perform better on financial data in every specific instance but it is possible (and easy) to show that machine learning works a lot better than linear regression on the financial data that Numerai gives away.* But showing that machine learning is better than linear regression still doesn’t prove that Numerai is a good idea, it just says that it might be. It’s possible that the data scientists signing up to Numerai aren’t good enough to outperform basic machine learning models. Numerai can easily average a collection of Python scikit-learn models ourselves without the help of our community of data scientists. We need to show that machine learning beats linear regression on financial data (showing that there exist modern methods that improve on the status quo), and that the Meta Model beats machine learning (showing that Numerai’s approach to collecting models from data scientists around the world is better than other hedge funds’ siloed approach). In other words, we need the Meta Model Supremacy Inequality to hold:meta model > machine learning model > linear modelBacktesting The Inequality With the data on Numerai, we ran a linear regression model and an XGBoost model. XGBoost is a machine learning model that builds a series of decision trees and ensembles them. Because it uses decision trees, it is able to learn non-linear structures in the data unlike the linear regression and tends to have strong performance on a variety of problems. To build the Numerai Meta Model, we first select the 200 user built models that are staked with our cryptocurrency, NMR, because data scientists who choose to risk money on their models by staking tend to have better performance (skin in the game helps). And we then filter those further finding the top 100 that work well together (for example, we drop the models that are very correlated because having lots of uncorrelated models produces a better ensemble). We then backtested** all the models on totally blind out of sample data and here’s what that looks like.Blind out of sample performance of models on Numerai’s dataset (meta model > machine learning model > linear model) It looks like the lines are in the right place and the inequality holds. The linear model gets a Sharpe 0.13, the machine learning model gets a Sharpe of 0.91, and the Meta Model gets a Sharpe of 1.55. Based on this test, the Numerai Meta Model reigns supreme. It looks that way. But it’s not magic. If you look closely, from February 2014 to October 2016 the Meta Model and basic machine learning model have made almost the same amount of money. That’s a long period for the Numerai users to not be beating a basic model in terms of return. If you take a look at the end of the backtest, you can can see all the models have a drawdown of similar magnitude. This was a particularly bad period for most quant funds; actually the worst period in 8 years. Many major quant funds lost big. And so would have Numerai’s Meta Model according to this backtest. And that’s not good because investors are interested in uncorrelated returns and when they talk to Numerai they’re expecting something special to come out of all the AI & crowdsourcing & blockchain we’re throwing at them. So it was a huge milestone to reach Meta Model supremacy with these very big Sharpe differences but it gave us a hedge fund that was basically a decent above average hedge fund that was quite correlated with other hedge funds. Nobody who works at Numerai has ever been ‘decent’ or ‘above average’ or ‘similar to peers’ in anything they’ve ever done so this wasn’t exactly a celebration moment, and we didn’t want to just sit on our hands and wait for another 2014 ‘good time for quant hedge funds’ kind of year.Extending The Edge With The Master of Robots I’ve been following Marcos Lopez de Prado since he released these slides 7 Reasons Most Machine Learning Funds Fail. It turns out, Marcos had been following Numerai and was a big fan of our approach. Marcos has two PhD’s. He founded and led Guggenheim Partners’ Quantitative Investment Strategies fund where he managed $13 billion. He was the first head of machine learning at AQR, the largest quant hedge fund in the world. He wrote the book on financial machine learning. And this year, he received the ‘Quant of the Year Award’ from The Journal of Portfolio Management. Bloomberg calls him The Master Of Robots. He came to visit us at Numerai in San Francisco a few months ago and spent a week looking at what we were doing, and telling us what we were doing wrong. Marcos really liked our approach and he ended up writing a whole paper on it with Frank Fabozzi. However, Marcos gave us some ideas for improving the dataset on Numerai. We always understood that more data would help the users build better models — that’s like data science 101. But just giving more data wasn’t enough. We needed to frame the problem we present in a better way to make the contributions from each user more ‘monetizable’. For example, if a Numerai data scientist puts effort into building a model that is exposed to momentum features but we then remove all momentum exposure in our portfolio optimization process, then that data scientist’s modeling efforts are wasted (i.e. not monetizable). Essentially, we had this data science problem where we weren’t asking the right question. And since Numerai is in charge of the question being asked, our data scientists couldn’t fix that themselves — we had to. (Remember all Numerai data is obfuscated — just a huge grid of numbers between 0 and 1 so only Numerai has control of the set up of the problem the inputs and targets.) We focussed on building a target variable that was much more monetizable and then we did the data science 101 thing and increased the number of features from about 40 to about 300. These changes together left us with way better data and way better targets so we released the new dataset to our data science community and deleted all the old stuff.Numerai’s Meta Model on new data and new targets And then the Numerai Meta Model backtest jumped to a Sharpe of 2.09. The changes significantly improved the performance of all users and the Meta Model. The meta model now performs quite well during in the 2018 drawdown, actually earning some money. But it’s still not magic. It sometimes has larger drawdowns than our previous Meta Model. It sometimes spends months being flat. But it’s now a much better hedge fund and when people analyze the backtest, it looks uncorrelated to other funds. And way above average. Getting all the technology and community in place to achieve Meta Model supremacy took years but adding the new data took months, and hundreds of brilliant data scientists modeling the new data took days. Meta Model supremacy puts us in a place where all we have to do is pour more data into the Numerai system and pose good problems and we will always be able to produce the best possible model on any given dataset. I’m sure all hedge funds will upgrade to machine learning eventually but they can’t upgrade to Numerai’s Meta Model. So that’s a nice defensible edge I hope we can keep, and it’s a defensible edge in an industry without many defensible edges.Watch The Video I presented some of these ideas and much more at Numerai’s first ever conference a few weeks ago. Here’s the video including my fireside chat with Marcos López de Prado, and an update on Numerai Compute from Anson Chu our VP of Engineering. (Also learn about a Numerai data scientist who’s also a NASA rocket scientist working on taking us to Jupiter’s moon.) https://medium.com/media/2e7ab081a12df44ec05f46a7c7f27af0/href*You can prove to yourself in minutes that linear models don’t do well on Numerai’s data by downloading Numerai’s data for free and running the example Python script.**These backtests are for illustrative purposes only and are all simulations and not representative of Numerai’s live trading or even the real backtests we share with our investors. You can trust that these backtests are out of sample and that the same optimization parameters are being applied to each model but don’t trust the numbers because there are hundreds of parameters involved in any backtest: cost assumptions, trading assumptions, risk levels, leverage assumptions, backtest period, stock universe choices that are not worth going into here but note that small alterations to these parameters can have very large effects eg halving leverage could halve return, changing cost assumptions could double Sharpe etc. Achieving Meta Model Supremacy At Numerai was originally published in Numerai on Medium, where people are continuing the conversation by highlighting and responding to this story.
Highlights from ErasureCon: CoinList Partners with Erasure for new Hackathon
Two weeks ago we hosted ErasureCon at a deconsecrated church in San Francisco’s Russian Hill district. Here are the Erasure highlights — full video below. Erasure is a new protocol for building staking and griefing applications on Ethereum. The protocol went live on main-net barely a month ago. Erasure already powers the Erasure Quant application and soon Numerai itself will be running on Erasure. The applications Numerai has built so far represent only a tiny part of what’s possible with the protocol. At ErasureCon, we brought together developers, investors and users to present our plans and discuss the future. Learn about how NMR has gone from a staking token on Numerai to a staking token for the web, how zero-day software exploits are a perfect use case for ErasureBay and the design philosophy of the protocol. I also sat down with Joel Monegro from Placeholder to talk about their investment thesis for Erasure and NMR. Watch the full Erasure presentation below. https://medium.com/media/22dd428fe5fd3b366c5c2cb331a424a6/href The Erasure team is going to be working on releasing ErasureBay, an information marketplace with the broad, mainstream application. But to enable developers to take it further with their own applications we’ve announced a new partnership with CoinList to run an online Erasure Hackathon open to anyone. Too often hackathons end with a bunch of gimmicky UIs and no lasting tools for developers. So the Erasure Hackathon is going to be different, and be focussed on building developer tools so that anyone without a coding background can implement Erasure across the web. 🏄♂️ Sign up for the Erasure Hackathon here Highlights from ErasureCon: CoinList Partners with Erasure for new Hackathon was originally published in Numerai on Medium, where people are continuing the conversation by highlighting and responding to this story.
Redesigning Ethereum’s Most Used Token
We’ve created one of the best tokens on Ethereum. And now we’re cutting the supply by ~50% and giving up control. Introducing NMR 2.0. Very few people are using Ethereum. Even when people are using an Ethereum application, they’re not necessarily using a new token. When you buy a CryptoKitty, you’re paying in ETH. When you trade on an exchange using the 0x protocol, you don’t need the ZRX token. The fact is, most Ethereum tokens are terminally useless. Here’s the good thing about Numerai’s NMR token: people are using it.NMR stakes — the number one staking token on Ethereum Here are all the bad things:NMR is centralizedNMR is an application not a protocolNMR token has no governance rightsNMR use is limited to Numerai data scientistsNMR can be diluted if Numerai mints millions of new NMRs These are all bad so we are going to fix every one of them with Erasure.Erasure Last month, we announced Erasure. It is a new decentralized data marketplace. It allows anybody to upload data, stake it with NMR, build a reputation, and earn money. Erasure takes everything that Numerai has been doing, and opens it up to the world on a decentralized protocol. The idea is not just to increase the usage of NMR, or to solve Numerai’s problems, but to create a radical new kind of data marketplace. The biggest risk in launching any new marketplace is not having enough buyers or sellers to get the ball rolling. But on Erasure, the Numerai community is ready to provide the first data feeds and Numerai will be a major buyer on day one. All of Erasure will be built on our token, NMR. We have recently hired a team to work exclusively on Erasure, with experience from Bridgewater, ConsenSys and Polymath. To do it right, they’re making some big changes to NMR.A New NMRErasure decentralizes NMR. This means that the NMR smart contract will no longer be controlled or upgradable by Numerai.NMR will no longer be a token to power Numerai’s hedge fund. Erasure turns it into a protocol. This means anyone can use NMR and build on top of Erasure, from sports prediction platforms to journalism.Erasure will be controlled by all NMR owners. NMR becomes a governance token.Erasure creates thousands of new use cases for NMR. Today, only Numerai’s data scientists need NMR. With Erasure, every data provider and every data buyer will need NMR.We’re going to end our ability to mint any more NMR. And to prevent dilution, we are cutting the supply of NMR by ~50% from 21 million to 11 million. We’ve taken a contrarian path and prioritized the usefulness of our token above its initial decentralization. As a result, we’ve created something rare: a token that makes sense and is being used. And now, Numeraire will be even better, more versatile, and will be controlled by the people it was made for. Sign up to receive updates on Erasure on erasure.xxx. Ask me anything on Reddit tomorrow. I’m doing an AMA on r/ethereum on 12/4/2018 at 11am California time. See you there. https://medium.com/media/b4b37fcaac471e1290810e23fe980950/href Redesigning Ethereum’s Most Used Token was originally published in Numerai on Medium, where people are continuing the conversation by highlighting and responding to this story.
An Unstoppable Predictions Marketplace — Introducing Erasure
An Unstoppable Predictions Marketplace — Introducing Erasure Today, Numerai is announcing a new unstoppable data marketplace called Erasure which uses the NMR token for staking. https://medium.com/media/7581f08239804f98f7f7f307581e1a12/hrefInformation Is Trapped Imagine you have done research on Apple’s hardware manufacturing. After investigating for some time, you know that iPhone sales in China will exceed all expectations, and you think that Apple is about to skyrocket. But today, the best thing you can do with this information is to buy Apple stock. In reality, the vast majority of information is difficult or impossible to express as a trade. If you download a trading application on your iPhone and buy shares of Apple, you’re exposed to all kinds of risks that lie outside the information you have. Your prediction about iPhone sales in China might turn out to be exactly right but you could still lose money on your trade. Currency fluctuations in the US dollar and Chinese Yuan could move against you. The US stock market may go down as a whole and crash your Apple shares along with it. Even if your information is great, there are many ways you could lose money on your trade. So you decide not to buy Apple shares, and your information never reaches the market. That’s bad for you, because you miss out on a rare and valuable opportunity. And it’s bad for the world, because it misses out on your information and a more efficient stock market. Instead of trading 100 shares of Apple on Robinhood, you’d much rather sell your prediction to a hedge fund who can get maximum value from your information. Hedge funds can hedge out the market risk, currency risk and other risks by making clever offsetting trades to get full value from the information. They can also trade your prediction with diversification — combining it with other predictions they have. Your own personal trade might only have a small expected return with a lot of volatility and risk. But your prediction might work fantastically inside a hedge fund’s diversified, low-volatility portfolio. In short, your information is much more valuable to a hedge fund than it is to you. And it’s better for the world for the hedge fund to have that information instead of you because a hedge fund with $500 million dollars can make much larger and smarter trades than you can with $5,000 in savings and a Robinhood account. If it’s so clearly better for individuals to sell information to hedge funds rather than trade themselves, where is the great data marketplace for predictions? It doesn’t exist. And it never could have existed until now.Asymmetric Information And Market Collapse You’re a hedge fund manager. You get an email that says, “over the last two years I have predicted Google’s daily stock price with 70% accuracy, and I’m willing to sell you my next prediction for $10,000”. You calculate that this next prediction, if its right, can make your fund an expected $1 million in one month. Do you buy the prediction? There are just too many problems with credibility to want to buy the next prediction. Did this person get lucky? Are they simply lying? If their past performance is real, where is the guarantee that it will continue? The seller of the predictions knows their quality but the buyer has asymmetric information. There is simply no way for the buyer to verify the quality of the predictions. Because of these questions around the credibility of the seller’s claims and the quality of his predictions, the people with genuinely predictive data cannot find a market for buyers of their information. No one believes what they have to say. The asymmetric information between the buyer and seller leads to what’s called market collapse. The seller’s information is trapped because no market can form around this data because no buyer can assess the quality (see Nobel Prize winner George Akerlof’s work for more on market collapse). The only solution right now for sellers of predictive information is to signal with brands or third parties in some way: “I used to work at Goldman Sachs, over the last two years I have predicted Google’s daily stock price with 70% accuracy, and I’m willing to sell you my next prediction for $10,000. You can see I also went Harvard if you add me to your professional network on LinkedIn.” Now you have to put some faith in LinkedIn, Harvard and Goldman Sachs. This kind of third party signaling with brands might work in some small way but it is not nearly enough to give a potential buyer of the prediction any kind of peace of mind when it comes to assessing the credibility of bold claims such as being able to predict Google with 70% accuracy. But maybe there is a technological solution for quality assessment. Maybe there is a third party that everyone trusts (or rather no one needs to trust). That technology is blockchain. Imagine a claim like this: “Over the last two years I have predicted Google’s daily stock price with 70% accuracy, you can verify this because I sent every prediction to the Ethereum blockchain ahead of time and no one can go back in time and change a blockchain. I also placed a large stake of $100,000 on these predictions on the blockchain. I’m willing to sell you my next prediction for $10,000 in cryptocurrency, and if you are unhappy with it for whatever reason, you can destroy my stake by destroying $10 worth of my stake for every $1 you destroy of your own cryptocurrency.” Today we are excited to announce a new protocol: an unstoppable, peer to peer, decentralized data marketplace for predictions. It will let individuals sell predictions about the world that everyone will trust, and make promises about their future performance that anyone can enforce. Using the blockchain, we have a solution to the market collapse problem. We are making a radical new kind of information market, and it’s called Erasure.The Erasure Protocol“The past was erased, the erasure was forgotten, the lie became the truth.”― George Orwell, 1984 Erasure is a new decentralized data marketplace. It allows anybody to upload predictions, stake them with cryptocurrency, build a track record that everyone can verify, and earn money. Commit to every prediction ahead of time In 2013, an open source project called Proof of Existence was launched. Proof of Existence allowed a user to timestamp a document using the Bitcoin blockchain. You could add your document to the Bitcoin blockchain, and thereby prove that the document existed at that time. Rob Wile from Business Insider wrote that Proof Of Existence is “perhaps the most straightforward example of a post-Bitcoin service using Satoshi’s blockchain”. On Erasure, you need to timestamp every prediction you upload, much like Proof of Existence. Erasure users will submit thousands of predictions; for example, a quant model for global equities might produce 10,000 stock predictions per day. It is too expensive to add all this data to Ethereum, so instead the data is uploaded to IPFS. IPFS is a decentralized file storage network, where Erasure stores each data feed. Erasure sends the address of these stored files, called a hash, to the Ethereum blockchain to get timestamped. By submitting just the the IPFS hash to Ethereum, the whole file of predictions gets a timestamp. (Think of the hash as like a URL pointing to a file containing all of the predictions on IPFS.) This process does one very important thing, it commits your prediction to the Ethereum blockchain at a certain time so you cannot alter your prediction later. If you submitted two years of daily Google stock price predictions to Erasure and you were 70% accurate, the whole world would be able to see that and check it for themselves (say against Yahoo! Finance data). Everyone would agree that your historical predictions occurred ahead of time, because all of your predictions are timestamped on Erasure. Now people can believe your claims because they don’t need to trust you. Reveal predictions later While exposing your predictions to the world would earn you credibility, it also means people could just use them without paying you. You somehow need to conceal your predictions while simultaneously committing to them. So on Erasure, every prediction is submitted using a commit and reveal scheme. You can commit to your prediction now and get it timestamped but you only have to reveal what your prediction was later when it is no longer valuable. Because everyone’s historical predictions are revealed but everyone’s most recent predictions are concealed, anyone can verify how good everyone’s prediction feed has been historically without ever being able to use the predictions. On Erasure, you can prove the quality of your predictions without giving anything valuable away. On Erasure, buyers can assess which prediction feeds have the highest quality and choose to buy the feed, which gives them a special key to see all the most recent predictions before anyone else — and therefore can trade on that information before anyone else. Stake tokens “The usual touchstone of whether what someone asserts is mere persuasion or at least a subjective conviction, i.e., firm belief, is betting. Often someone pronounces his propositions with such confident and inflexible defiance that he seems to have entirely laid aside all concern for error. A bet disconcerts him. Sometimes he reveals that he is persuaded enough for one ducat but not for ten. For he would happily bet one, but at 10 he suddenly becomes aware of what he had not previously noticed, namely that it is quite possible that he has erred.” — Immanuel Kant, Critique of Pure Reason Suppose you’re a hedge fund manager and you find a prediction feed on Erasure which really does have 70% accuracy predicting the daily price of Google. Are you comfortable buying the feed? Because the feed is on Erasure, you know for sure that every prediction was made ahead of time with a tamper proof timestamp to prove it but you don’t know whether what you’re seeing is a result of luck. Maybe the creator of this prediction feed has created 100,000 other feeds and you’re looking at the only one that got lucky. Maybe 50,000 of his other feeds had less than 50% accuracy on Google, and only a handful had more than 60% accuracy. He’s trying to sell you his feed which has 70% accuracy but that 70% accuracy is a result of pure luck. You don’t want to buy a prediction feed that simply got lucky with its historical predictions; the luck is very unlikely to continue. Erasure needs some kind of resistence to this problem to give buyers more quality assurance. To prevent the 100,000 prediction feed exploit, a kind of Sybil attack, Erasure uses staking. If every prediction on Erasure required some cryptocurrency to be staked at risk, then sellers of predictions couldn’t afford to make 100,000 prediction feeds in order to try to get lucky. So on Erasure, every prediction is staked with cryptocurrency. By staking cryptocurrency, the seller of a prediction feed puts something at risk if things go wrong (“skin in the game”) producing incentive alignment and higher quality feeds. Allow buyers discretionary recourse You’re a hedge fund manager, you find the Google feed with 70% accuracy and you see that a $10,000 stake was made on the feed at the time that the first predictions were submitted. You are more confident than ever about it’s quality. But you still can’t be sure that the 70% accuracy will continue, and really no one can be sure about that including the seller but it does appear that this prediction feed has the potential to be worth trading on. But you do want some kind of recourse if things go wrong. If the feed accuracy ends up being 50% you’re going to be upset you bought it. You’ll lose money paying the seller for the prediction, and you’ll lose money trading as well. So Erasure has something called “griefing”. Griefing lets you destroy the seller’s stake by destroying some of your own. When a seller stakes their prediction feed on Erasure, they choose what’s called a “griefing factor”. The griefing factor is the degree to which the seller is happy for the buyer to be able to destroy their stake. A griefing factor of “1:10” means that for every $1 a buyer destroys of their own money, $10 of the seller’s stake will get destroyed. So on Erasure, when a buyer starts to buy a live prediction feed, the seller gives them a special right to destroy their stake according to the griefing factor. Especially agressive buyers might be especially agressive with griefing. For example, a buyer who buys a feed with 70% historical predictive accuracy on Google might start destroying the seller’s stake if the accuracy drops below 65% and other buyers might be more lenient. As the marketplace for predictions develops, sellers will adjust their griefing factors to signal quality or fear. Some sellers might give griefing factors of 1:50 to express confidence in the quality of their predictions others might give griefing factors of 1:1 afraid of meeting aggressive buyers. But why would a buyer grief at all if he loses money in order to harm the seller? Isn’t it irrational to harm yourself in order to harm others? No, it sometimes is rational especially in the setting of Erasure which is a repeated game. Buyers are not just buying one prediction they are subscribing to a feed of ongoing predictions. If a seller gives a bad prediction, it may indeed be rational for the buyer to grief the seller if doing so increases the likelihood that the seller will improve the quality of their subsequent predictions. Griefing on Erasure is similar to an Ultimatum Game, which in a repeated setting tends to result in fair behavior. Griefing is the final recourse for buyers on Erasure, and the final element to produce aligned behavior to make it possible for a market for predictions to take form.Numerai and Erasure Erasure uses no speculative technology; it is something that can work today. In fact, Numerai has been using a special case of the Erasure protocol for over a year.Predictions uploaded to Numerai vs Erasure: both are just csv’s backed by cryptocurrency stakes. Just over one year ago, Numerai released the Numeraire token (NMR), which is used by data scientists to stake stock market predictions submitted to Numerai’s hedge fund. Since then, NMR has become one of the most used utility tokens on Ethereum. In June, there were more NMR stakes in number and dollar value than any other Ethereum application including CryptoKitties.NMR, already the number one staking token on Ethereum NMR also worked exactly as designed. The predictions staked with the NMR token dramatically outperform predictions not staked with NMR. In our backtests, predictions staked with NMR have a Sharpe of 2.09 vs Sharpe of 1.66 for unstaked predictions. Skin in the game works. But NMR has a problem; it is a token designed just for Numerai. Only Numerai data scientists use NMR for staking, only Numerai can buy predictions and destroy stakes, and all prediction datasets need to be uploaded to Numerai’s servers. In a word, NMR is centralized — and that’s bad because it limits NMRs potential. But today we are announcing that we are decentralizing NMR by making NMR the native token of Erasure. NMR will become the staking token for Erasure, a decentralized marketplace for predictions where any individual can sell any prediction feed to any hedge fund.Right now, NMR is the token for Numerai. With Erasure, NMR becomes the token for the entire hedge fund industry.Future For NMR holders, Erasure is a whole new use case for NMR in a bigger, braver, decentralized future. For the hedge fund industry, Erasure is an unstoppable decentralized data marketplace to buy high quality, trustable new data feeds. Erasure is a way to free all the trapped information, help it reach the market and improve the world but Erasure could be used for anything from sports predictions to crypto predictions to weather forecasts to whistleblowing. There are no restrictions on the kinds of data feeds you can submit, and no usage can be censored or stopped. Marketplaces are two-sided but Erasure starts with a big buyer on the buy side (Numerai) and sellers on the sell side (Numerai’s users who have already staked over $1 million worth of NMR in thousands of different stakes across billions of predictions). But as prediction feeds build verifiable track records on Erasure, traditional hedge funds will have no choice but to participate in the marketplace as well. If quants on Quantopian build long track records predicting the S&P 500 with high returns and low volatility and staked these predictions with large amounts of NMR, traditional hedge funds like Two Sigma will have no choice but to take notice. Demand from hedge fund’s will lead to more sellers and vice versa. Hedge funds will compete aggressively to buy predictions and over time Erasure will become the highest paying way to sell predictions. So the best, most unusual data sources will be drawn to sell on Erasure thereby attracting even more hedge fund buyers.Join Us We are looking for people who understand that the highest leverage way to make a difference in the world is to work on the invisible things — the systems behind the system. We have some open job positions for people who see that finance is the system behind it all, and that rebooting finance will have a huge impact on every other industry as well. If you’re interested in helping to build Erasure email email@example.com. If you’re interested in contributing to the project as a developer, data scientist or hedge fund, email us on firstname.lastname@example.org, join our Telegram channel @NMR_Official, or sign up to receive email updates on erasure.xxx. An Unstoppable Predictions Marketplace — Introducing Erasure was originally published in Numerai on Medium, where people are continuing the conversation by highlighting and responding to this story.
Numerai’s New Token Supply
Numerai just locked up 3 million NMR tokens (worth ~$45 million) until 2028; here’s why. In a cryptoeconomic application, users make choices about what to do with a crypto token — that’s the crypto part. But the token needs to be a scarce resource for these decisions to be meaningful — that’s the economics part. Understanding the supply of a token is therefore essential for making good decisions in a cryptoeconomic system such as Numerai. So we want to explain the supply of NMR, and our intentions for it.3 million NMR lockup The NMR smart contract has a cap of 21 million NMR tokens. Numerai initially gave away 1 million NMR to our users but the smart contract also allows Numerai to mint new NMR each week (~100,000 per week). Because we haven’t needed it, the NMR we are allowed to mint recently exceeded 4 million NMR. We want to use this available NMR to continue to grow the Numerai network in new ways such as with new airdrops or new partnerships with investors who have experience growing decentralized applications. But we do not need all 4 million NMR to do this so we have decided to lock up 3 million NMR (worth ~$45 million) until 2028. We want this lock up to be irreversible and verfiable so we locked up the NMR using the NMR staking contract on the Ethereum blockchain. This means it is impossible for Numerai to access this NMR until 2028, and anyone can verify this fact for themselves. To do the lockup, we created a dummy round in tournament 0, round 5 with a resolve date of 2028, and staked 3 million NMR on it. You can verify the lock up transaction here: https://etherscan.io/tx/0x2a7892a5315c5fb46471f7b524b6c1854f1bf6724018447417d1cb0f323f01a4, and you can check the stake here: https://etherscan.io/address/0x1776e1f26f98b1a5df9cd347953a26dd3cb46671#readContract in the `getStake` function with arguments 0, 5, 0x000000000000000000000000000000000001ee9d, and “\0”. You can verify the resolve date of the round by calling `getRound` with arguments 0, 5. The final returned timestamp is the resolution date in Unix time (number of seconds since January 1, 1970).Community Just this week, we had the highest number of NMR stakes ever — up 4x since January. NMR is one of the most used utility tokens on Ethereum, and we want to keep it that way. Overall, this lock up takes a large supply of NMR out of the control of Numerai shifting power towards the community. We hope that this helps simplify the supply of NMR, and helps our community better understand it. Numerai’s New Token Supply was originally published in Numerai on Medium, where people are continuing the conversation by highlighting and responding to this story.
Numeraire, The Cryptocurrency Powering The World Hedge Fund
In 2017, Numerai released a new API that allows people around the world to submit predictions from machine learning models to power our hedge fund. In 2018, we are focussing on our cryptocurrency, Numeraire (NMR). We are going to make NMR more valuable to use, and we are going to dramatically grow the number of data scientists who use it.Numeraire Usage Every user on Numerai has an opportunity to stake NMR tokens on their predictions as a way to express the confidence they have in their model. If their predictions are good, they earn money and their NMR is returned. If their predictions are poor, their NMR is destroyed. The most important usage metric for us is whether the biggest earners of NMR are also the biggest stakers i.e. the whales are the users. And this is exactly what is happening on Numerai. If you go to the leaderboard on Numerai and sort by the career earnings of NMR, you can see that out of the top 15 NMR earners, 7 are currently staking NMR. So nearly half of the participants that have the largest amounts of NMR are staking it every week. I don’t know of a single cryptocurrency project where the token is used so widely and frequently by its largest holders.On Numerai, the whales are the users (blue means currently staking NMR) A big reason for this usage is that Numerai did not sell its token in an ICO to speculators with no intention of using it. Instead we gave it away proportionally to the best data scientists on Numerai — the people who were most likely to use it. And on the day of the launch, NMR was not only usable but used immediately by its recipients. Some of our data scientists have staked NMR every single week since launch (see for example, https://numer.ai/steppenwolf).Numeraire Effectiveness The reason we ask data scientists to risk NMR by staking it on their models is so that only the good models get staked. And this is happening too. When Numerai users submit predictions, they also submit predictions on a test set. Users do not get feedback on their performance on this test set nor do they even know what our data represents; this ensures that backtests on this test set are rigorous. We analyzed the Sharpe ratio of the unstaked models vs the staked models based on the backtest of the test set. Here is the performance:Sharpe ratio of unstaked models: 1.66Sharpe ratio of models staked with NMR: 2.09 Our backtests have many parameters such as how we combine the models together, how we optimize the portfolio, and how we estimate trading costs. With the above Sharpe ratios, we assume that we equal weight the models, assume no trading costs, and don’t do any optimization. This difference in Sharpe means that staked models earn significantly more returns with lower volatility in the backtest. For example, for the same 10% of volatility, the staked models would earn 20.9% whereas the unstaked models would earn just 16.6% — a big difference for a hedge fund. NMR is a huge success story for a utility token. It is widely held and being used by its holders. NMR has led to the exact intended outcome of the white paper we published one year ago. It engineers a precise behavior and improves the performance of models on Numerai.A 5x Increase Because staking NMR is such a powerful signal, we want to put an even greater focus on it this year. We have just increased the staking payout to 2000 NMR + 6000 USD. At the current price of NMR on secondary markets, this change is a 500% increase in payouts in the staking tournament. Starting today, the only way to earn NMR is by staking NMR. This means staking NMR becomes even more valuable, and more NMR will now go to the users who are holding it and staking it.The total earnings available on Numerai is over $140,000 per month making it the most well-paid data science tournament on the planet. Numeraire, The Cryptocurrency Powering The World Hedge Fund was originally published in Numerai on Medium, where people are continuing the conversation by highlighting and responding to this story.
Numerai’s Master Plan
When Numerai launched in December 2015, it was a one page website that looked like a Kaggle data science competition except everything was black and more cinematic. We didn’t want it to look like any other hedge fund in the world — because we weren’t. We spent all this time on design and shooting videos (like this and this), and it seemed like a big distraction. The core idea of Numerai was to give away all of our data for free, and let anyone train machine learning algorithms on it and submit predictions to our hedge fund. This was a very counterintuitive idea. We already had our own internal machine learning models on the data so it seemed like a distraction to open it up to the world. When we originally decided to pay our data scientists in bitcoin, the price of bitcoin was $400 (now $5600). The idea of paying people in bitcoin was confusing to most people. It seemed like a distraction. When we announced we were creating our own cryptocurrency on Ethereum in February, the price of ether was $4.65 (now $300). Many of our users and investors had never heard of Ethereum, and they couldn’t understand why we would want our own cryptocurrency. There had never been a hedge fund with their own cryptocurrency. It seemed like a distraction. Some of our distractions have already proven themselves to be outrageously successful, and others are still developing, but they actually aren’t distractions at all. They are all part of the plan. — The Master Plan - Monopolize intelligence, Monopolize data, Monopolize money, Decentralize the monopoly, 1+2=3 and 4 would be awesome. — The Roadmap - The master plan is the high level guide to what we’re trying to do over the next several years. We’re mainly focussed on part one right now: monopolize intelligence. Here’s our current roadmap. — Numeraire Q2|17. — The reason we created our own cryptocurrency (called Numeraire / NMR) is because it connects directly to the first part of our master plan. It not only grows our community of data scientists but also improves the quality of intelligence they provide. Monopolizing intelligence involves getting all the talented data scientists in one place working together on one hedge fund rather than duplicating work in a zero sum game across multiple hedge funds. Aside from building a large data science community, monopolizing intelligence also means having the intelligence be of high quality. We need the predictions that our data scientists provide to be directly useful to our hedge fund’s trading strategy. Numeraire improves the intelligence on Numerai because of the nature of how it is used. It is not really a currency. It is a token to access the staking tournament on Numerai. In the staking tournament, data scientists stake Numeraire on their predictions to express their confidence that their model will perform well on live data. If their models perform well, they earn more money (paid in ether). If their models perform poorly, their Numeraire is destroyed. After the first few stakes of Numeraire were made, it was immediately clear that Numeraire staking was working. The average quality of predictions increased and we were able to isolate the best models to include in our meta model — the model that combines them all together. The underlying staked predictions when combined achieve performance that we cannot match with our internal models. Since Numeraire can be used to earn money via staking, it has value in its own right. Including the value of the Numeraire rewards, Numerai has paid out millions of dollars, and is now the most well paying data science tournament in the world. This financial incentive has lead to huge growth. In the last three months, the number of data scientists on Numerai has doubled, engagement has doubled and our Slack channel has doubled. A data scientists on Numerai recently uploaded the 1 millionth prediction set. That equates to over 40 billion predictions. We now have 30,000 data scientists on Numerai. That is 100x more than any other hedge fund in the world, and we are not yet two years old. Due to the extraordinary success of Numeraire staking, we are doubling the payouts in the staking tournament from 3000 USD per week to 6000 USD per week. This number is now 6x higher than when we launched Numeraire. To emphasize staking even further, the only way to earn USD on Numerai (paid in ether) is to enter the staking competition. In the general tournament, we are increasing the Numeraire payouts from 1500 NMR per week to 2000 NMR per week. This gives new users new possibilities to earn Numeraire to compete in the staking tournament. Overall, the changes represent a ~20% increase in payouts. Numeraire can be earned by anyone competing in our tournaments but there is now a new way to earn it as well. We have open sourced core parts of Numerai, and our community can now earn Numeraire bounties by making contributions to the codebase. Finally, Numeraire is also the beginning of part 4 of the master plan, which is to decentralize Numerai. Right now it’s impossible to have a real hedge fund be decentralized because stocks are not traded on blockchains, prime brokers don’t give leverage on blockchain assets and so on. In the future, this will probably change and more of Numerai could be decentralized and connected to the Numeraire token. — API Q4|17. — — @FEhrsam Numerai started with a Kaggle style data science competition but that was never the end goal. Numerai needs to use the predictions from our data scientists live in our hedge fund without having access to any of the algorithms that built the models. To do this, we needed to create an entirely new data science tournament design. We needed to automate payments using cryptocurrency, and we needed to move away from Numerai being a website for people but rather an API for AIs. The goal for Numerai was to be an API that any artificial intelligence could use to control capital in the economy. The API would pass datasets to the AIs to train on and the AIs would submit predictions back to Numerai. And the AIs would get paid in the only currency they can use and understand: cryptocurrency. Since the API interacts with the Numeraire smart contract on Ethereum, people will able to build applications on top of Numeraire. For example, a data scientist could build a server that automatically downloads new data from Numerai, trains a machine learning algorithm, stakes Numeraire on the set of predictions, and repeats this process forever earning more and more money and NMR for the data scientist, and adding more and more intelligence to Numerai’s hedge fund. This isn’t speculative; we wouldn’t be surprised if one of our data scientists built a automated system exactly like this within a few days of the API launching. Numerai’s new GraphQL API written in Elixir will be released on October 31st. — Reputation Q1|18. — Over time, data scientists who regularly achieve concordance, originality, consistency and strong live logloss will also earn bonuses as their reputation grows. We have had that statement on our help page for many months, and we’re still thinking about how to implement a reputation system on Numerai. Our data scientists have excellent ideas for how it should work. The idea is data scientists are building reputations for themselves as time progesses. Those reputations are valuable to us as another data point for our meta model so we want data scientists with the best reputations to earn the most. — Compute Q2|18. — You will be able to send Numeraire to an Ethereum smart contact and get computational power for almost no cost. Wow. The idea is to create an AWS AMI which has all the software you would need to do machine learning, and also all the connections to Numerai’s API that you would need to send predictions live automatically to Numerai forever. With Compute, the idea of discrete tournaments will start to fade away. Numerai would be able to ping any AI connected to it for new predictions at any time. Even more futuristic versions of this could use decentralized computing resources like Golem. Compute will create an entirely new use case for Numeraire which is directly related to improving machine learning models, increasing engagement, and achieving the goals of the master plan. I’m doing an AMA on r/ethereum at 12pm California time today (October 13th). Ask me anything. Numerai’s Master Plan was originally published in Numerai on Medium, where people are continuing the conversation by highlighting and responding to this story.
More Numeraire (#NMR) News
|Market Update: Bitcoin And Ethereum Struggles To Recover, AR And NMR Soa...
Despite the market condition, top currencies, Bitcoin and Ethereum, struggle to regain their values. On the other hand, some altcoins, AR, and NMR are showing a significant gain charm over 14-days.
Since the Fed Reserve decided to raise interest rates and the May inflation report, Bitcoin has been floating in a small zone between $19,000 and $20,000. But since last month, the price of Ethereum, which had a great November like bitcoin, has been floating between $1,100 and $1,000.
Meanwhile, it made an effort and struggled to recoup its value, but the price of Ethereum couldn't rise beyond $1,100. CoinGecko data shows ETH is currently trading at $1,073.58, and in the past 7 and 14 days, ETH has lost 10.6% and 0.6% of its value, respectively.
Significant Growth in AR and NMR
Arweave (AR), one of the top 100 crypto assets by performance and market capitalization, is booming, while two of the most popular currencies are faltering. Per the statistics from CoinGecko, AR has reached $11 and increased by 10% in one day, 15% in seven days, and 32% in fourteen.
A decentralized storage network, Arweave aims to provide a platform for the long-term archival of data. The Arweave network pays 'miners' to keep the network's data permanently stored using its native coin, named AR.
Similarly, Numeraire (NMR) is also leading in week gainers. It displays a noteworthy increase of 122% over a week. NMR is currently trading at $19.37, per the statistics of Coinmarketcap.
Additionally, during the pre...