Math formula

Proof of trading PoT token model calculations

Mathematical Model for Conversion

Validated by ChatGPT. Read details here.

For max trading volume for 100% of oMAIGA unlocks, 10,000,000 BNB trading volume on whitelisted DEX LP is calculated (appr. $8b based on $800/BNB).

Monte Carlo simulation for market variability

To model uncertainty and randomness in market behavior, we introduce a stochastic noise factor. This ensures random market fluctuations are factored into the unlock model, preventing overly rigid outcomes.

Final oMAIGA conversion calculation

Bringing all components together, the final unlock formula is:

This ensures that:

✅ Unlocks scale with trading volume

✅ Buy-side demand boosts unlock rates

✅ Manipulative behaviors reduce unlock amounts

✅ Market fluctuations introduce realistic unpredictability

We believe by using this novel approach of a new type of token model is akin to taking a long-term approach involves addressing the cold-start problem by gradually increasing depth and liquidity in the market.

💡 As depth and liquidity improve, more traders are attracted to the market, resulting in increased trading volume over time, thus, improving the bullishness and sentiment for MAIGA token and Maiga ecosystem.

Key Features of the PoT token model

✅ Bullish Momentum Rewards

• Strong Buy Volume (Buy Ratio > 0.75) & Surging Volume (+1m/day) → 1.4x unlock boost

• Moderate Buy Volume (Buy Ratio 0.6-0.75) & Uptrend (+500k/day) → 1.2x unlock boost

• Impact: Encourages traders to drive sustained bullish momentum.

✅ Bearish Protection Against Dumping

• Weak Buy Volume (Buy Ratio < 0.45) & Declining Volume (-50k/day) → 0.8x unlock slowdown

• Extreme Sell-Off (Buy Ratio < 0.35 & Volume Collapse -100k/day) → 0.5x severe unlock reduction

• Impact: Prevents large-scale sell pressure by discouraging exits during crashes.

✅ Scenario-Specific Adjustments for Manipulation Prevention

• Flash Loan Attacks → 0.3x unlock penalty

• Wash Trading Detection → 0.6x unlock penalty

• Liquidity Sniping → 0.75x unlock penalty

• Bot-Driven Momentum Trading → 1.3x unlock boost (to encourage algorithmic liquidity)

• Impact: Protects from fake volume tricks while rewarding legitimate long-term liquidity strategies.

Real world market behavior

Market Scenario	Dynamic Unlock Adjustments	Impact
Bull Market (Strong Buying)	1.4x unlocks when buy ratio > 0.75	Encourages sustained FOMO
Healthy Uptrend	1.2x unlocks when buy ratio > 0.6	Promotes stable growth
Sideways Market	1.0x unlocks remain unchanged	No forced incentives
Mild Bear Market	0.8x unlocks when buy ratio < 0.45	Prevents exit dumps
Severe Bear Market	0.5x unlocks when buy ratio < 0.35	Locks down selling pressure
Flash Loan Attack	0.3x unlocks	Blocks instant liquidity abuse
Wash Trading	0.6x unlocks	Removes fake trading benefits
Liquidity Sniping	0.75x unlocks	Prevents thin liquidity targeting
Bot Momentum Trading	1.3x unlocks	Rewards consistent volume creation

Formula to prevent wash trading at CEX

Initially on TGE, 100% of whitelisted DEX LP volume is used for calculation to prevent wash trading on CEXs and DEXs LPs with little liquidity

Instead of using raw volume, apply a weighted formula that prioritises organic liquidity sources using Volume-Weighted & Liquidity-Adjusted Data. This method reduces influence from low-trust exchanges.

Example:

• Binance (W=1.0), Coinbase (W=0.9), OKX (W=0.8), KuCoin (W=0.6), Suspicious Exchange (W=0.2)

• If reported volume is 100M, 50M, 30M, 20M, and 40M, then:

Buy/Sell ratio normalization formula

Since buy/sell volume ratios impact unlocks, they must be weighted & smoothed. This prevents sudden manipulation attempts from spiking buy ratios.

Monte Carlo Simulation (100 Runs)

To validate the model, a Monte Carlo simulation was performed with 100 runs, incorporating:

1. Randomised Buy Volume & Trading Volume. Each run sampled buy/sell ratios from real-world crypto datasets.

2. Scenario-Specific Modification. Each run randomly introduced flash loan attacks, wash trading cycles, and whale movements to test the model’s robustness.

3. Standard Deviation Calculations. The unlock trajectories were analyzed to identify expected unlock ranges and extreme market deviations.

✅ Bullish Market Conditions → Unlocks increase up to 1.4x due to high buy pressure.

✅ Bearish Market Conditions → Unlocks reduce up to 50% to prevent dumping.

✅ Wash Trading & Flash Loans Are Penalized → Unlocks are slashed by 40-70% in manipulation scenarios.

✅ Monte Carlo Confidence Intervals Show Stable Outcomes → Despite market noise, the model remains predictable and robust.