Jim Simons Trading Secrets 1.1 MARKOV Process

Jim Simons is considered to be one of the best traders of all time he has even beaten the like of Warren Buffet, Peter Lynch, Steve Cohen and Ray Dalio. Jim's Renaissance Technologies and Medallion Fund are purely quantitative trading funds. The methodology he and his team uses are highly secretive but there are lots of hidden clues and approaches that could be found in the book The Man Who Solved the Market

In this video we dissect the Markov process approach found in the book in simple terms and how we can use it to find amazing strategies with codes written in Python for research and Tradingview pinescript for application. We do a practical real world example in SPY.

The codes for both the Python file and Pinescript can be found here:

Timecodes
00:00 - Intro
01:00 - Book Evidence and Interpretations
02:36 - Markov Strategy results on Course
04:37 - What is Markov Process, Examples
04:28 - Markov Probabilities Examples
07:15 - Markov Trading Example
09:10 - Transition Matrix Probabilities
10:29 - Application Of Markov in Python for SPY
12:40 - Transition matrix for SPY
15:20 - Applying single condition on Pinescript
16:13 - Interpretation of Results and Improvement

Our Course's

[quantprogram]

The second part of the video that talks about how Jim Simons generates simulated data can be found on our Youtube channel. If you have no experience in python watch our full "Algorithmic trading in Python Zero to Hero" Video also found on our channel

[gamuchiraindawana]

Please keep making more videos about reinforcement learning concepts this is amazing, no on else on youtube is breaking down these concepts as gracefully as you just did, phenomenal stuff man. Thank you

[eduardomafra]

The mind-blowing video opened my mind to the quant model trading in terms of application to the real world. I've read a lot about it but never had this insightful explanation.

[StephenDoty]

I suspected Jim Simons also does correlation trading based on what mathematicians call 'action limits' within 'activity networks'. When two financial instruments or asset classes deviate from known means and standard deviations over time, an 'action limit' can be set in the algorithm, for example: changes in the price of a 10 year T-note has shown a strong correlation to the price of copper divided by the price of gold. If a rare event occurs beyond three standard deviations say, calculated by a computer program, then it is highly probable the price of copper will fall and the price of gold will rise so the correlation with the price the T-note regresses toward the mean more. The 'action limit' looks like mu = +/- 2.5 STD/square root of N, where N is the number of values in the sample of copper/gold ratios, say. So the computer will automatically short coper and buy gold at certain times until the action limit is no longer triggered. It has to do with 'critical path analysis' where vertices in the path represent different activities to be performed, as in the computer generating orders to buy, sell, short, etc.

[zchn]

The code " data[ (data["state"] == "up") & (data["state"].shift(-1) == "down") ] " will return rows in the pandas DataFrame where the current state is "up" and the next state (shifted by one position) is "down". It should be "up_to_down" instead

[VikBrummer]

What a fantastic expansion of the Markov process thank you so much!

[Regular.Biceps]

I know you wanted to keep it simple for the users but you back tested the strategy on the same sample on which you have calculated odds.. in reality, the sample is continuously changing and it might not behave in the same manner in future with same odds

Greay explanation for Markov odd through... One of the finest segment of this video

[chrisarets]

Interesting approach to looking at trading data!

[majorkuntz]

Possibly a coincidence but random forrest also gives a 57% probability on the same ticker.

[Ash__]

Great content, will definitely keep your course in mind.

[konrad]

Ok, i'm gonna be a quant, you convinced me. Gratest video of all times.

[RavindraElicherla]

Awesome Video. Keep up the great work!!!

[gamuchiraindawana]

Beautiful work.

Now we as your audience can help optimise the code and share findings.

For example there is no need to calculate up_to_up and then calculate up_to_down, simple statistics allows us to perform this instead Probability(up_to_down) = 1 - Prob(up_to_up). So if you calculate one, you know the other. Bayesian statistics.

[YeeYeez]

Good thing I took linear algebra. Markov chains was the one thing I actually learned and enjoyed learning. Thank you for the explanation and coding walk through.

[chandannath]

never thought that Markov model would be deployed in trading. have read the Hidden Markov chain long ago

[NishantChoudharyJi]

Incredible explanation about the most powerful strategies of Most successful trader of the world 🌎

[nabilsn]

i like to marcov process, he is defining the probability for the price to go same origin, we can apply this for polynomial regression, as the price has good probability to get back to middle line.

[Kokoroko]

Mindblowing content I have so many strats and ideas I have never been able to correctly back test, will look at your other content with huge enthusiasm have a great weekend

[kanakTheGold]

Thanks Quant, was a very informative video! I propose 2 corrections and 1 recommendation -
1. CORRECTION: shift(-1) actually takes you to NEXT element in the series. So in your program, you need to rename series up_to_down as down_to_up and vice versa.
2. CORRECTION: The last date of 'data' series doesn't have a future date to compare against. So real count of UP/DOWN days is 1 less than the original series length. Just check if last day state is UP or DOWN and subtract 1 from the initial length obtained.
3. RECOMMENDATION: English (and most languages) are read left to right. So Transpose the transition matrix you have defined, so we consider the row-indices are current day states & column-indices as next date states.

[parkerhenry]

It’s cool to notice that this mean reverting strategy looks to be performing better under higher volatility conditions.