---
product_id: 141312597
title: "The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution"
price: "€ 61.09"
currency: EUR
in_stock: true
reviews_count: 13
url: https://www.desertcart.gr/products/141312597-the-man-who-solved-the-market-how-jim-simons-launched
store_origin: GR
region: Greece
---

# The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution

**Price:** € 61.09
**Availability:** ✅ In Stock

## Quick Answers

- **What is this?** The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution
- **How much does it cost?** € 61.09 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.gr](https://www.desertcart.gr/products/141312597-the-man-who-solved-the-market-how-jim-simons-launched)

## Best For

- Customers looking for quality international products

## Why This Product

- Free international shipping included
- Worldwide delivery with tracking
- 15-day hassle-free returns

## Description

NEW YORK TIMES BESTSELLER Shortlisted for the Financial Times /McKinsey Business Book of the Year Award The unbelievable story of a secretive mathematician who pioneered the era of the algorithm–and made $23 billion doing it. The greatest money maker in modern financial history, no other investor–Warren Buffett, Peter Lynch, Ray Dalio, Steve Cohen, or George Soros–has touched Jim Simons’ record. Since 1988, Renaissance’s signature Medallion fund has generated average annual returns of 66 percent. The firm has earned profits of more than $100 billion, and upon his passing, Simons left a legacy of investors who use his mathematical, computer-oriented approach to trading and building wealth. Drawing on unprecedented access to Simons and dozens of current and former employees, Zuckerman, a veteran Wall Street Journal investigative reporter, tells the gripping story of how a world-class mathematician and former code breaker mastered the market. Simons pioneered a data-driven, algorithmic approach that’s swept the world. As Renaissance became a market force, its executives began influencing the world beyond finance. Simons became a major figure in scientific research, education, and liberal politics. Senior executive Robert Mercer is more responsible than anyone else for the Trump presidency, placing Steve Bannon in the campaign and funding Trump’s victorious 2016 effort. Mercer also impacted the campaign behind Brexit. The Man Who Solved the Market is a portrait of a modern-day Midas who remade markets in his own image, but failed to anticipate how his success would impact his firm and his country. It’s also a story of what Simons’s revolution will mean for the rest of us long after his death in 2024.

Review: Great book that revealed many secrets of Simons’s unprecedented success - I have been live-trading, with my Fidelity IRA account, using the signals generated by the models as introduced in Forecasting and Timing Markets: A Quantitative Approach (ASIN:B0875JBWBQ ). Started from March 09 this year, I have achieved a net profit of 24.54% as of April 24, which is impressive given the market volatility induced by the COVID-19. Coincidentally, I learned from an online post that Simons’s Medallion Fund also achieved an over 24% return during this same period of time. I was motivated to find out more about his Medallion Fund and thus bought this book. I eagerly read through the entire book so that I could assess how different his quantitative approach is against the AlphaCovaria System I have been relying on as mentioned above. I am so grateful for Mr. Zuckerman who dug out so many details about how Simons’s models have been built. Here is a summary of what I have learned from a quantitative trader’s perspective: (1) First, a little background. While at IDA during his earlier career, Simons and his colleagues wrote a research paper that determined that markets existed in various hidden states that could be identified with mathematical models. At IDA, they built computer models to spot "signals" hidden in the noise of the communications of the United States' enemies. This was the precursor to Simons’s later persistent pursuit to testing the approach in real life. (2) Performance-wise, Simons has been the most successful one in trading, given the performance comparisons of this list: Jim Simons (Medallion) 39.1%, George Soros (Quantum Fund) 32%, Steven Cohen (SAC) 30%, Peter Lynch (Magellan Fund)29%, Warren Buffett (Berkshire Hathaway) 20.5%, and Ray Dalio (Pure Alpha) 12%. One of the factors that Simons could succeed so much is that he is a strongly principled person with a strong belief in "Work with the smartest people you can, hopefully, smarter than you... be persistent, don't give up easily." So he is not only a great mathematician but also a great visionary and business manager. (3) Their model dev process: By 1997, Medallion's staffers had settled on a three-step process to discover statistically significant moneymaking strategies, or what they called their trading signals: (1) Identify anomalous patterns in historic pricing data, (2) make sure the anomalies were statistically significant, consistent over time, and nonrandom , and (3) see if the identified pricing behavior could be explained in a reasonable way. (4) Trading frequency: Medallion made between 150,000 and 300,000 trades a day, but much of that activity entailed buying or selling in small chunks to avoid impacting the market prices. (5) Data granularity: They use five-minute bars as the ideal way to carve things up. Their data hunter Laufer's five-minute bars gave the team the ability to identify new trends, oddities, and other phenomena, or, in their parlance, nonrandom trading effects. (6) Holding period: Medallion still held thousands of long and short positions at any time. Its holding period ranged from one or two days to one or two weeks. The fund did even faster trades, described by some as high-frequency, but many of those were for hedging purposes or to gradually build its positions. Renaissance still placed an emphasis on cleaning and collecting its data, but it had refined its risk management and other trading techniques. (7) Their performance as measured by Sharpe ratio. 1990s, Medallion had a strong Sharpe ratio of about 2.0, double the level of the S&P 500. But adding foreign-market algorithms and improving Medallion's trading techniques sent its Sharpe soaring to about 6.0 in early 2003, about twice the ratio of the largest quant firms and a figure suggesting there was nearly no risk of the fund losing money over a whole year. No one had achieved what Simons and his team had-a portfolio as big as $5 billion delivering this kind of astonishing performance. In 2004, Medallion's Sharpe ratio even hit 7.5, a jaw-dropping figure. Medallion had recorded a Sharpe ratio of 2.5 in its most recent five-year period, suggesting that the fund's gains came with low volatility and risk. (8) Their portfolio composition. They started with commodity, bond, and currency, but later expanded into equities, which became the major source of profits after many years of efforts. (9) Does Simons strictly stick to their models? In general, yes, but he made calls when he saw models were malfunctioning due to extreme market conditions. (10) How have their models worked under various market conditions? Their models are mostly neutral, which was made possible by making quick trades only to eliminate unforeseeable events. They claimed that they could make models that would work with long-term investments, but it seems that they have not done so. (11) What is the most secret juice with their models? Medallion found itself making its largest profits during times of extreme turbulence in financial markets. They believed investors are prone to cognitive biases, the kinds that lead to panics, bubbles, booms, and busts. "We make money from reactions people have to price moves." They look for smaller, short-term opportunities-get in and get out. The gains on each trade were never huge, and the fund only got it right a bit more than half the time, but that was more than enough. "We are right 50.75 percent of the time... but we're 100 percent right 50.75 percent of the time," Mercer told a friend. "You can make billions that way." (12) How long was their learning curve? Simons spent 12 full years searching for a successful investing formula, without much success until he and Berlekamp built a computer model capable of digesting torrents of data and selecting ideal trades, a scientific and systematic approach partly aimed at removing emotion from the investment process. (13) Size of their computing infrastructure​. On page 248, it says their computer room was the size of a couple of tennis courts. I arrived at a guestimate that they might have about ~13,000 servers, computed like this: 2x78x27 (two tennis courts) x 0.6 (total area occupied by racks) / (2x4 (rack area)) x 40 (servers per rack) = 12,636. This should not be too far away from what they have. I strongly encourage every serious quant to read through the entire book for a lot of other secret juices.
Review: Worthwhile Investment History - I enjoyed Mr. Zuckerman’s effort very much - it was a book I didn’t particularly want to put down and it makes for a fun and quick read. Think folks would do well to consider their perspectives/objectives for the book. I’m not primarily a professional money manager, have an M.S. in finance but no advanced training in complex mathematics. I think quantitative rules-based investing systems have significant value. I came to the book under no illusion that it was going to reveal any “secrets” that could be put into action and that held true. But I found it to be a nicely done history of quantitative investing & how technology enabled the scale and complexity. Here are some specific notes: 1. I’m not sure the title does justice to the book or to participants. There’s no doubting the obscenely amazing performance of the Medallion funds. Mr. SImons, as founder and CEO, certainly has earned a large place in financial history. But while the book does a terrific job communicating Mr. Simons’ early work as a cryptographer & his academic achievements in respect of geometers etc., it seems to describe a man who had a vision for how the market might be solved & drove the funding/infrastructure for realization - but not a man who actually developed the specifics of the fund’s model. It really seems to be several of the other key characters who poured endlessly over pricing history, identified & tested anomalies and wrote the algorithmic codes (beginning with commodities & fixed income, equities later on). 2. The author does a very good job, IMHO, of discussing concepts like factor investing, statistical arbitrage, paired trades, hedges, market neutral, etc. And he takes the time to nicely reference some of the underlying math for those who have the interest, touching on concepts ranging from differential equations to mean reversion to Brownian motion to embedded Markov processes. The author doesn’t purport to try and teach readers how they might use those ideas - appropriately so - but it’s meaningful perspective. 3. Not surprisingly, there’s a dichotomy re “how” the market was “solved.” There won’t be much new here for traders. At the broadest level of generality, certain pricing anomalies were identified & incorporated into algorithms that turned the raw data into trading signals. Harnessing computing power, the fund trades a ton, such that it doesn’t need to make much on each trade and only needs to get it right a bit over half the time - returns are then amplified by liberal employment of leverage; the systematic model is trained - application of machine learning - to continue to improve precision on its own and to determine trades/positions. Beyond that, though - & it shouldn’t be folks’ expectation- the book doesn’t go granular on the model’s inputs. It can’t and doesn’t give away the particulars of the black box. The author should be credited for his tackling of the funds’ initial problems with slippage and for reporting on how the funds had no choice but to move into equities in order to attain such massive AUM. Also great history on early and superior efforts to obtain/recreate pricing data & good discussion of the core fund’s preference for extremely short holding periods. 4. There’s some pretty riveting investing history here, ranging from early developments in technical analysis to the long and steady rise of fundamentals-based investing to the profound skepticism with which systematic quant trading was treated for an exceptionally long time. 5. The narrative is at times beautiful , at others choppy and abrupt. Probably too many cases of basically “the fund was in trouble” to “the fund was thriving”. It’s like, “oh, that’s good” 6. In terms of personal biography, my understanding is that Mr. Simons is intensely private - under those constraints, the author does well in tracing his life and career, though for me, a truly strong and well developed portrait remains elusive. The author comes closer to that mark in telling the stories of several of the other key participants in the firm’s rise over time. 7. Later in the book, a ton of space is devoted to Robert Mercer’s public politics and how it impacted the firm. I thought it was interesting stuff, but some may find it loses focus, e.g. there’s quite a bit on Rebekah Mercer that just doesn’t have much relation to the core story. This was an ambitious endeavor and Mr. Zuckerman should be credited for that. As personal biography, it’s s fine effort. As financial history, I’d characterize it as informative, accessible and entertaining. But I’m not sure I’d say it’s of huge importance. The telling of the story isn’t, in my view, likely to have any real impact on the methods and practice of finance. But for finance junkies, there’s a ton of on point info, perspective, teaching and fun. Thanks much to the author.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #33,560 in Books ( See Top 100 in Books ) #45 in Biographies of Business & Industrial Professionals #68 in Finance (Books) |
| Customer Reviews | 4.5 out of 5 stars 5,328 Reviews |

## Images

![The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution - Image 1](https://m.media-amazon.com/images/I/81ZmhjQI2LL.jpg)

## Available Options

This product comes in different **Media Language, Media Format** options.

## Customer Reviews

### ⭐⭐⭐⭐⭐ Great book that revealed many secrets of Simons’s unprecedented success
*by Y***N on April 27, 2020*

I have been live-trading, with my Fidelity IRA account, using the signals generated by the models as introduced in Forecasting and Timing Markets: A Quantitative Approach (ASIN:B0875JBWBQ ). Started from March 09 this year, I have achieved a net profit of 24.54% as of April 24, which is impressive given the market volatility induced by the COVID-19. Coincidentally, I learned from an online post that Simons’s Medallion Fund also achieved an over 24% return during this same period of time. I was motivated to find out more about his Medallion Fund and thus bought this book. I eagerly read through the entire book so that I could assess how different his quantitative approach is against the AlphaCovaria System I have been relying on as mentioned above. I am so grateful for Mr. Zuckerman who dug out so many details about how Simons’s models have been built. Here is a summary of what I have learned from a quantitative trader’s perspective: (1) First, a little background. While at IDA during his earlier career, Simons and his colleagues wrote a research paper that determined that markets existed in various hidden states that could be identified with mathematical models. At IDA, they built computer models to spot "signals" hidden in the noise of the communications of the United States' enemies. This was the precursor to Simons’s later persistent pursuit to testing the approach in real life. (2) Performance-wise, Simons has been the most successful one in trading, given the performance comparisons of this list: Jim Simons (Medallion) 39.1%, George Soros (Quantum Fund) 32%, Steven Cohen (SAC) 30%, Peter Lynch (Magellan Fund)29%, Warren Buffett (Berkshire Hathaway) 20.5%, and Ray Dalio (Pure Alpha) 12%. One of the factors that Simons could succeed so much is that he is a strongly principled person with a strong belief in "Work with the smartest people you can, hopefully, smarter than you... be persistent, don't give up easily." So he is not only a great mathematician but also a great visionary and business manager. (3) Their model dev process: By 1997, Medallion's staffers had settled on a three-step process to discover statistically significant moneymaking strategies, or what they called their trading signals: (1) Identify anomalous patterns in historic pricing data, (2) make sure the anomalies were statistically significant, consistent over time, and nonrandom , and (3) see if the identified pricing behavior could be explained in a reasonable way. (4) Trading frequency: Medallion made between 150,000 and 300,000 trades a day, but much of that activity entailed buying or selling in small chunks to avoid impacting the market prices. (5) Data granularity: They use five-minute bars as the ideal way to carve things up. Their data hunter Laufer's five-minute bars gave the team the ability to identify new trends, oddities, and other phenomena, or, in their parlance, nonrandom trading effects. (6) Holding period: Medallion still held thousands of long and short positions at any time. Its holding period ranged from one or two days to one or two weeks. The fund did even faster trades, described by some as high-frequency, but many of those were for hedging purposes or to gradually build its positions. Renaissance still placed an emphasis on cleaning and collecting its data, but it had refined its risk management and other trading techniques. (7) Their performance as measured by Sharpe ratio. 1990s, Medallion had a strong Sharpe ratio of about 2.0, double the level of the S&P 500. But adding foreign-market algorithms and improving Medallion's trading techniques sent its Sharpe soaring to about 6.0 in early 2003, about twice the ratio of the largest quant firms and a figure suggesting there was nearly no risk of the fund losing money over a whole year. No one had achieved what Simons and his team had-a portfolio as big as $5 billion delivering this kind of astonishing performance. In 2004, Medallion's Sharpe ratio even hit 7.5, a jaw-dropping figure. Medallion had recorded a Sharpe ratio of 2.5 in its most recent five-year period, suggesting that the fund's gains came with low volatility and risk. (8) Their portfolio composition. They started with commodity, bond, and currency, but later expanded into equities, which became the major source of profits after many years of efforts. (9) Does Simons strictly stick to their models? In general, yes, but he made calls when he saw models were malfunctioning due to extreme market conditions. (10) How have their models worked under various market conditions? Their models are mostly neutral, which was made possible by making quick trades only to eliminate unforeseeable events. They claimed that they could make models that would work with long-term investments, but it seems that they have not done so. (11) What is the most secret juice with their models? Medallion found itself making its largest profits during times of extreme turbulence in financial markets. They believed investors are prone to cognitive biases, the kinds that lead to panics, bubbles, booms, and busts. "We make money from reactions people have to price moves." They look for smaller, short-term opportunities-get in and get out. The gains on each trade were never huge, and the fund only got it right a bit more than half the time, but that was more than enough. "We are right 50.75 percent of the time... but we're 100 percent right 50.75 percent of the time," Mercer told a friend. "You can make billions that way." (12) How long was their learning curve? Simons spent 12 full years searching for a successful investing formula, without much success until he and Berlekamp built a computer model capable of digesting torrents of data and selecting ideal trades, a scientific and systematic approach partly aimed at removing emotion from the investment process. (13) Size of their computing infrastructure​. On page 248, it says their computer room was the size of a couple of tennis courts. I arrived at a guestimate that they might have about ~13,000 servers, computed like this: 2x78x27 (two tennis courts) x 0.6 (total area occupied by racks) / (2x4 (rack area)) x 40 (servers per rack) = 12,636. This should not be too far away from what they have. I strongly encourage every serious quant to read through the entire book for a lot of other secret juices.

### ⭐⭐⭐⭐ Worthwhile Investment History
*by M***N on November 18, 2019*

I enjoyed Mr. Zuckerman’s effort very much - it was a book I didn’t particularly want to put down and it makes for a fun and quick read. Think folks would do well to consider their perspectives/objectives for the book. I’m not primarily a professional money manager, have an M.S. in finance but no advanced training in complex mathematics. I think quantitative rules-based investing systems have significant value. I came to the book under no illusion that it was going to reveal any “secrets” that could be put into action and that held true. But I found it to be a nicely done history of quantitative investing & how technology enabled the scale and complexity. Here are some specific notes: 1. I’m not sure the title does justice to the book or to participants. There’s no doubting the obscenely amazing performance of the Medallion funds. Mr. SImons, as founder and CEO, certainly has earned a large place in financial history. But while the book does a terrific job communicating Mr. Simons’ early work as a cryptographer & his academic achievements in respect of geometers etc., it seems to describe a man who had a vision for how the market might be solved & drove the funding/infrastructure for realization - but not a man who actually developed the specifics of the fund’s model. It really seems to be several of the other key characters who poured endlessly over pricing history, identified & tested anomalies and wrote the algorithmic codes (beginning with commodities & fixed income, equities later on). 2. The author does a very good job, IMHO, of discussing concepts like factor investing, statistical arbitrage, paired trades, hedges, market neutral, etc. And he takes the time to nicely reference some of the underlying math for those who have the interest, touching on concepts ranging from differential equations to mean reversion to Brownian motion to embedded Markov processes. The author doesn’t purport to try and teach readers how they might use those ideas - appropriately so - but it’s meaningful perspective. 3. Not surprisingly, there’s a dichotomy re “how” the market was “solved.” There won’t be much new here for traders. At the broadest level of generality, certain pricing anomalies were identified & incorporated into algorithms that turned the raw data into trading signals. Harnessing computing power, the fund trades a ton, such that it doesn’t need to make much on each trade and only needs to get it right a bit over half the time - returns are then amplified by liberal employment of leverage; the systematic model is trained - application of machine learning - to continue to improve precision on its own and to determine trades/positions. Beyond that, though - & it shouldn’t be folks’ expectation- the book doesn’t go granular on the model’s inputs. It can’t and doesn’t give away the particulars of the black box. The author should be credited for his tackling of the funds’ initial problems with slippage and for reporting on how the funds had no choice but to move into equities in order to attain such massive AUM. Also great history on early and superior efforts to obtain/recreate pricing data & good discussion of the core fund’s preference for extremely short holding periods. 4. There’s some pretty riveting investing history here, ranging from early developments in technical analysis to the long and steady rise of fundamentals-based investing to the profound skepticism with which systematic quant trading was treated for an exceptionally long time. 5. The narrative is at times beautiful , at others choppy and abrupt. Probably too many cases of basically “the fund was in trouble” to “the fund was thriving”. It’s like, “oh, that’s good” 6. In terms of personal biography, my understanding is that Mr. Simons is intensely private - under those constraints, the author does well in tracing his life and career, though for me, a truly strong and well developed portrait remains elusive. The author comes closer to that mark in telling the stories of several of the other key participants in the firm’s rise over time. 7. Later in the book, a ton of space is devoted to Robert Mercer’s public politics and how it impacted the firm. I thought it was interesting stuff, but some may find it loses focus, e.g. there’s quite a bit on Rebekah Mercer that just doesn’t have much relation to the core story. This was an ambitious endeavor and Mr. Zuckerman should be credited for that. As personal biography, it’s s fine effort. As financial history, I’d characterize it as informative, accessible and entertaining. But I’m not sure I’d say it’s of huge importance. The telling of the story isn’t, in my view, likely to have any real impact on the methods and practice of finance. But for finance junkies, there’s a ton of on point info, perspective, teaching and fun. Thanks much to the author.

### ⭐⭐⭐⭐⭐ The Quant Renaissance
*by B***A on January 17, 2020*

Fans of the financial markets will be fascinated by the story of an obscure academic who quits his comfortable life to toil for years as an investing also-ran but then cracks the code of the markets on to becoming the most successful investor of a generation. A child prodigy who graduated MIT by the time he was 20, Jim Simons would go to on become a US code breaker and a successful theoretical mathematician. Ultimately his risk averse, middle class lifestyle would gnaw at him. The author wittily pillories the life of an academic by reprising the joke, “What’s the difference between a PhD in mathematics and a large pizza? A: A large pizza can feed a family of four.” While not destitute, Simons pined for something bigger. He felt he could figure out the market but getting there was a convoluted process. Gregory Zuckerman’s analysis of the lives involved and tactics used make for an excellent read. The foundational theory would come from a 1960’s paper Simons wrote calling for an unemotional approach that favored pattern fitting the different states that the market seemed to periodically enter. The first big step was hiring Lenny Baum. Baum applied his specialty in Markhov models that use the most recent price data to make strong approximations on the future. At the same time, Simons knew he would need larger data sets so hired a Cal Tech grad named Greg Hullender and later fellow Stony Brook mathematician Sandor Strauss. After some departures, Stony Brook mathematicians Jim Ax and Henry Laufer strengthen these models and, with the help of UC-Irvine’s Rene Carmona, added the concept of kernel methods (an early machine learning process that sought out complex patterns and correlations). A big breakthrough came in 1990 with Berkeley professor Elwyn Berlekamp’s ability to pick up on minute oddities in the market. He was a leading force in convincing Simmons to stop worrying about why anomalies existed and to just profit from them. Such data overfitting, or trying to explain too much, is a big quantitative hurdle for many. The author has this great anecdote to show the issue: Quant investor David Leinweber later would determine that US stock returns can be predicted with 99 percent accuracy by combining data for the annual butter production in Bangladesh, US cheese production, and the population of sheep in Bangladesh and the US. Soon after, in 1990, Medallion would gain 55.9%. From the 1988-2008, after fee returns would be 40%. The firm’s success further evolved with the hiring of Peter Brown and Bob Mercer from IBM. Profiting on retracements, when stocks are overbought or sold, and finding patterns in unexplainable or odd patterns became their forte. Finding such quantitative peculiarities created a moat for their algorithms as other investors simply tried to but could not explain such mispricing. Mercer summed up the strategy simply, “we’re right 50.75 percent of the time . . . but we’re 100 percent right 50.75 percent of the time.” Interestingly, during this time, their models actually underperformed as minor bugs like the hardcoding of the SP value held them back. Once fixed, Renaissance would enhance their models to learn from correlated assets in the market, per one insider: “This interconnectedness is hard to model and predict with accuracy, and it changes over time. RenTec has built a machine to model this interconnectedness, track its behavior over time, and bet on when prices seem out of whack according to these models.” The firm also developed basket options which allowed them to cut the downside of their trades and proved to be a more tax efficient structure. Zuckerman does a great job bringing up other competitors. LTCM, who had a similar approach until their demise, typically double downed on their losing trades. Renaissance on the other hand tended to cut risk and used less leverage. David Shaw, a former academic and Morgan Stanley alumni, also competed in the same style. He took seed capital from Donald Sussman’s Paloma Partners and grew his firm into a formidable competitor. Interestingly, one early DE Shaw programmer was Jeff Bezos. Simons’ neighbor George Soros and his lieutenant, economics PHD Stanley Drukenmiller, are profiled for their analytical macro approach. And fascinating to hear that Simons lamented the steady success of equity options trader Berne Madoff (pre-scandal). The most interesting part is the waxing and waning support Simons has for his models. The firm’s success hinged on developing models whose output they could not explain. However, during the LTCM and 2008 crises, Simons decided to ignore the models and lessen his risk in order to survive. There is a great scene where while vacationing, a retired Simons calls up his money manager to see if they should hedge since the market seemed volatile. This from a man who made billions solving the market.

## Frequently Bought Together

- The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution
- A Man for All Markets: From Las Vegas to Wall Street, How I Beat the Dealer and the Market
- The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It

---

## Why Shop on Desertcart?

- 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016
- 🌍 **Shop Globally** — Access 737+ million products across 21 categories
- 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price
- 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members)
- 🔒 **Secure Payments** — Trusted payment options with buyer protection
- ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews

**Shop now:** [https://www.desertcart.gr/products/141312597-the-man-who-solved-the-market-how-jim-simons-launched](https://www.desertcart.gr/products/141312597-the-man-who-solved-the-market-how-jim-simons-launched)

---

*Product available on Desertcart Greece*
*Store origin: GR*
*Last updated: 2026-10-08*