# Holder reward ($MMLM Reward)

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Once you’ve completed the first $MMLM purchase, you officially become part of the MMLM network. From the moment of your initial token purchase, our system automatically begins to accumulate $MMLM rewards for you.

**Your initial holder rewards come from a 1% tax on every $MMLM transaction (both buy and sell) made by users who’ve joined the network after you.**\
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:bulb: To provide a clearer illustration, let's consider a scenario involving 1,000 holders who participate in purchasing $MMLM tokens. 

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According to our Multi-Level Reward Model, any wallet that trades $MMLM will pay a 1% tax per transaction. This tax is automatically transferred to our commission pool, where it accumulates over time together with other rewards. Eventually, the accumulated funds are distributed among all [preceding wallets](https://mmlm.gitbook.io/mmlm/mmlm-reward-model/preceding-wallets) (higher-ranked wallets).\
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**Here's how the distribution works:**

* Holder 1000: This holder pays a 1% tax on their transactions, and the tax amount is distributed among holders ranked from 1 to 999.
* Holder 999: This holder pays a 1% tax on their transactions, and the tax amount is distributed among holders ranked from 1 to 998.

      ....

* Holder 4: This holder pays a 1% tax on their transactions, and the tax amount is distributed among holders ranked from 1 to 3.\
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  The **Reward Calculation Formula** is as follows:

{% code overflow="wrap" %}

```
  Your holder reward = (Your balance / Balance of all preceding wallets) * Total Reward amount
```

{% endcode %}

This formula ensures that the distribution of rewards takes into account the proportionate balance of each holder relative to the total balance of higher-ranked holders.

By implementing this mechanism, we aim to incentivize early adopters and provide them with additional benefits. Early participants not only have the opportunity to acquire $MMLM tokens at an advantageous price but also have the potential to earn more rewards from holders in lower ranks. We believe that this model promotes fairness and encourages users to actively engage with the ecosystem, ultimately contributing to its growth and success.


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