Jim Simons Trading Secrets 1.1 MARKOV Process

Source: Jim Simons Trading Secrets 1.1 MARKOV Process, QuantProgram, 20:12, uploaded 2023-05-06, Watch Later position 865.

QuantProgram presents a Markov process as a small, readable clue to the secretive methods of Renaissance Technologies. The video begins with the Medallion Fund’s reported 39 per cent annual return after fees across three decades, then moves from a passage in Gregory Zuckerman’s The Man Who Solved the Market to a simple calculation on SPY. The result is a lesson in conditional probabilities, followed by a trading rule built from a run of down days.

Clues from Renaissance Technologies

The speaker treats Jim Simons as one of the greatest traders in history and attributes the fund’s performance to a purely quantitative approach. The fund’s methods remain private, so the video looks for clues in Zuckerman’s book. A passage about a mathematician who worked with Simons describes a Markov chain in which each step remains uncertain whilst a model can still estimate future steps with some accuracy. The same passage mentions a stochastic equation.

The video also describes a mean-reverting approach from another mathematician in the book. Prices tend to return after an unusually high or low move, so the strategy buys futures contracts when they open at unusually low prices. The source captions render the mathematicians’ names inconsistently, and the note keeps the description at that level.

QuantProgram then shows reported results for a course strategy during the 2007 and 2008 recessionary period. The speaker cites returns of 152 per cent and 136 per cent, whilst the displayed chart compares the strategy with an S&P 500 buy-and-hold line that falls sharply. The same strategy is shown across later periods and is said to work well in high-volatility conditions. This section also promotes QuantProgram’s course and its Q5 strategy, so the performance figures remain claims made in promotional material.

A process that keeps the current state

A Markov process describes a sequence in which the probability of the next state depends on the current state. Earlier states fall out of the model once the current state contains the information needed for the forecast. The video uses weather as a first example. Tomorrow’s weather depends on today’s state in the simplified model, whilst yesterday’s weather has no separate role.

The longer example follows a traveller moving between home, a shop, and work. A human remembers the route that led to the shop and can use that history to choose what comes next. Markov, the video’s fictional traveller, only sees the shop as his current state. He therefore has a 50 per cent chance of going home and a 50 per cent chance of going to work. At home he goes to the shop with probability 1, and at work he also goes to the shop with probability 1. The possible routes form the process, while the current location supplies the state used for the next step.

From daily returns to a transition matrix

The trading example reduces each day to a positive or negative percentage move. The numbers first appear as hypothetical probabilities. After a positive day, the next day has a 0.7 probability of being positive and a 0.3 probability of being negative. After a negative day, the video assigns a 0.2 probability to another negative day and a 0.8 probability to a positive day.

The two probabilities in each row must add up to 1. The result is a transition matrix:

Historical data can supply these values by counting each type of transition. The video also mentions machine-learning models, which it associates with the methods described in the book. It keeps the demonstration to direct counts so that the process remains visible in a few lines of code.

The SPY calculation in Python

The example moves into an Anaconda notebook with yfinance, pandas, and numpy. The speaker downloads SPY data from 2010 to 2022, although he says that a shorter period or a recessionary period such as 2008 could be selected for comparison. The data includes open, high, low, close, adjusted close, and volume. The calculation uses adjusted close and its percentage change to produce a daily return.

The notebook assigns an up state when the daily return is greater than or equal to zero and a down state otherwise. It then counts up days, down days, and each adjacent transition. The four resulting probabilities are presented as 54 per cent for up to up, 45 per cent for up to down, 57 per cent for down to up, and 42 per cent for down to down. The largest value is the move from a down day to an up day, yet the speaker considers these four results too close to 50 per cent to support a strong trade on their own.

A longer sequence and a Pine Script rule

The next test asks what happens after five or six consecutive down days. The video reports a 66 per cent probability that the following day is up. QuantProgram treats that figure as useful enough to turn into a rule. The Pine Script entry condition checks a sequence in which each close is below the preceding close. The position closes when the next day’s close rises above the current close.

The displayed backtest reports a 46 per cent return with a 5 per cent drawdown and about 20 trades since 1994. The earlier notebook used 2010 to 2022, so the precise sample behind the later result remains unclear in the source. The small number of trades also makes the result sensitive to the selected period and to the exact entry and exit rules.

The speaker then suggests testing four or five down days, mixing sequences such as up-down-up, and building an exit condition from its own transition probabilities. More conditions can narrow the entries. Applying a rule across more stocks can spread the result across a larger set of opportunities. He refers to an earlier strategy applied to 25 and 90 stocks as an example of reducing drawdown through breadth.

What the model can and cannot show

The final section returns to the book’s clues. QuantProgram presents its Q5 strategy as a combination of mean reversion and a Markov-inspired sequence of days, then withholds the full method as course material. Charts for QQQ, Microsoft, and Walmart are offered as further examples of the single-condition approach. The speaker infers that Simons may combine many conditions across many instruments, while also admitting that the fund’s actual method remains unknown.

The Markov property is a modelling choice. A daily up or down state discards the size of the move, volatility, volume, market regime, and the history that may matter to the next return. The transition probabilities can change as the market changes. The source supplies no transaction costs, slippage, out-of-sample test, benchmark definition for the later returns, or confidence interval. Its 66 per cent figure comes with 20 displayed trades, so it describes a narrow backtest rather than a proven trading edge.

The description presents the video as educational material and warns that past results do not predict future returns. The linked code page is an email-gated QuantProgram landing page. It promises the Python and Pine Script files, while the files themselves were unavailable through the page during source review.

Further reading / references

• Gregory Zuckerman, The Man Who Solved the Market, named in the video description as the source of the Renaissance Technologies clues.
• QuantProgram’s Markov Process code page, linked in the video description and presented as the source for the Python and Pine Script files. The page requires an email submission before delivering the code.