👀👀 [Request for Review]

[kcw-grunt]

No description provided.

[kcw-grunt]

Nice @lshpaner !
Please add the list of reviewers here. There is no PR per se.
But, I can use it to promote.

[lshpaner]

@coblee
@losh11
@DavidBurkett
@wangxinxi
@josikie
@kcw-grunt

This is for a pending open-source Python library (main.py) that I would like to upload to PyPI. It gives users flexibility to simulate Litecoin user growth over a period of time into the future given BTC hashrate and some other cool plots. Please review functions and white paper and let me know what you all think. Thanks!!!

[josikie]

@lshpaner Wow! I read the white paper. That's amazing and looks so cool! I have questions.

1. What is stochastic processes?
2. What is stochastic noise?

Thank you!

[lshpaner]

@josikie Thank you for the feedback!

What is stochastic processes?

A stochastic process is a mathematical concept used to describe systems that evolve over time in a way that involves randomness or uncertainty. Unlike deterministic processes, where the future state of the system is entirely predictable given its current state, stochastic processes incorporate elements of chance.

Example
Consider the stock market. The price of a stock on any given day depends on a variety of unpredictable factors, such as news, investor behavior, and economic indicators. Even though there are patterns and trends, there's an element of randomness that makes it impossible to predict with complete certainty. This makes stock prices an example of a stochastic process.

What is stochastic noise?
Stochastic noise refers to the random fluctuations or variations that are inherent in a stochastic process. It's essentially the "background" randomness that affects the outcome of the process but isn't directly caused by any specific factor.

Example
In the context of financial markets, stochastic noise might refer to the small, unpredictable price movements that occur due to random trades, market sentiment changes, or other minor factors. These fluctuations can obscure the underlying trends and make it harder to distinguish between true signals and random "noise."

Quick Summary

Stochastic Process: A system that evolves over time with inherent randomness.
Stochastic Noise: The random fluctuations or variations within that stochastic process.
These concepts are fundamental in fields like finance, physics, and even in this specific work on cryptocurrency modeling, where randomness and uncertainty play a significant role.

[wangxinxi]

Very impressive. Where is the visualisation?

[lshpaner]

Very impressive. Where is the visualisation?

@wangxinxi Thank you! As of right now, in the readme file:

https://github.com/litecoin-foundation/litecoin_analytics/blob/main/README.md

I can, however, create a jupyter notebook which will essentially be the same demo.

[wangxinxi]

Is there any strong correlation between hash rate and active users?

[lshpaner]

@wangxinxi

I went ahead and created new class for correlating actual LTC data with bitcoin hashrate --> CryptoCorrelation() which is new feature that the library will have.

If we are to look at only last 3 months worth of user data (based on active LTC user addresses from CoinMetrics), and connect this with CoinGecko's BTC hashrate, we have from 05/01/2024 - 08/01/2024, the following:

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If we cast a wider net and do a look back from January 1, 2023 through January 1, 2024, the correlation increases by a whopping 42%:

---

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That being said, below is the correlation between median predicted users and BTC hashrate (I added this as a feature inside the updated simulate_monte_carlo_growth_with_hashrate() function as well as ability lock in random state (seed) of experiment.

Given, as inputs:

    initial_users=1000,
    carrying_capacity=100000,
    base_growth_rate=0.10,
    time_steps=365,
    num_simulations=1000,

Correlation between hash rate and median user growth: 0.9235

Predicted Year over Year Growth

Time	Median Prediction	Lower 95% CI	Upper 95% CI	Lower 99% CI	Upper 99% CI
2024-08-11	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000
2024-08-12	1007.595648	987.626117	1025.924861	981.756035	1030.213331
2024-08-13	1006.114466	966.507823	1045.214634	953.698048	1057.997866
2024-08-14	1006.094482	948.750471	1065.063558	934.421504	1088.104310
2024-08-15	1010.662228	941.035548	1087.033279	914.053157	1114.865286
...	...	...	...	...	...
2025-08-07	99999.999973	99999.986887	100000.000000	99999.905807	100000.000000
2025-08-08	99999.999895	99999.932025	100000.000000	99999.186360	100000.000000
2025-08-09	99999.999943	99999.919709	100000.000000	99999.093680	100000.000000
2025-08-10	99999.999977	99999.977508	100000.000000	99999.705785	100000.000000
2025-08-11	99999.999983	99999.976671	100000.000000	99999.847955	100000.000000

---

Predicted Quarterly Growth

Time	Median Prediction	Lower 95% CI	Upper 95% CI	Lower 99% CI	Upper 99% CI
0	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000
1	1007.595648	987.626117	1025.924861	981.756035	1030.213331
2	1006.114466	966.507823	1045.214634	953.698048	1057.997866
3	1006.094482	948.750471	1065.063558	934.421504	1088.104310
4	1010.662228	941.035548	1087.033279	914.053157	1114.865286
...	...	...	...	...	...
361	99999.999973	99999.986887	100000.000000	99999.905807	100000.000000
362	99999.999895	99999.932025	100000.000000	99999.186360	100000.000000
363	99999.999943	99999.919709	100000.000000	99999.093680	100000.000000
364	99999.999977	99999.977508	100000.000000	99999.705785	100000.000000
365	99999.999983	99999.976671	100000.000000	99999.847955	100000.000000