The Market Cycle Gene Forecasting Engine (MCGF) is a research-oriented computational tool designed to analyze and forecast tokenomic gene frequencies across market cycles.
In modern tokenomics research, smart contract features minting logic, fee mechanisms, permission structures, risk vectors are treated as economic “genes”. These genes mutate, rise, fall, or completely disappear depending on:
- Market liquidity
- Narrative cycles
- Speculation waves
- Scam evolution patterns
- Behavioral incentives
- Developer trends
MCGF allows researchers to track these genes over time and predict their future behavior using lightweight, transparent, interpretable forecasting models.
Unlike AI-based predictors or noisy black-box tools, MCGF focuses on:
- clarity,
- interpretability,
- transparent assumptions,
- and reproducible analysis.
It is designed for economists, blockchain researchers, auditors, and anyone studying tokenomics evolution as a scientific phenomenon.
A tokenomic gene is a measurable behavior encoded in a smart contract. Examples:
MINT_UNBOUNDED| unlimited minting capabilityFEE_BASIC| simple transfer feesFEE_HIGH_RISK_PATTERN| scam-prone tax patternsBLACKLIST_PRESENT| selective transfer blockingPROXY_UPGRADEABLE| future mutation capabilityREBASING_SUPPLY| supply elasticity
Each gene is a trait that influences:
- economic incentives
- centralization level
- manipulation vectors
- user fairness
- systemic risk
- price dynamics
Gene frequencies across the ecosystem change over time and those changes correlate strongly with market cycles.
MCGF is built on the observation that the crypto market behaves like an evolutionary environment:
- low liquidity
- low retail exposure
- high technical focus
- scam-genes suppressed
- governance/safety genes dominate
- capital inflow
- creative experimentation
- new tokenomic patterns emerge
- FOMO extremes
- rapid mutation
- scam-gene explosion (honeypots, dynamic-tax, mint exploits)
- liquidity extinction
- predatory genes die off
- sustainable genes remain
MCGF helps quantify and forecast these evolutionary shifts.
MCGF was designed to answer questions like:
- Which tokenomic genes will dominate the next bull cycle?
- Are dangerous genes (honeypot patterns, blacklist logic) increasing or declining?
- How fast is a given gene evolving across cycles?
- Which tokenomic species are likely to re-emerge?
- How do different genes correlate with different market phases?
This can support:
- academic research
- market intelligence
- scam prediction
- DeFi risk modeling
- tokenomics design
- macro-tokenomics forecasting
MCGF includes two interpretable forecasting models:
Fits a simple regression:
frequency = a + b * time
This model is ideal for:
- steady gene drift
- macro-directional trends
- multi-cycle forecasting
Characteristics:
- interpretable
- stable
- transparent
Computes the mean of the last k observations:
forecast = avg(last k frequencies)
Useful for:
- noisy data
- short-term smoothing
- low-variance genes
Perfect alignment, no Unicode issues, CMD-compatible.
Preferable to “mystery-box” ML.
Analyze entire ecosystems at once.
Compatible with annotated market cycles.
Pure Python, portable and lightweight.
market-cycle-gene-forecaster/
│
├── mcgf/
│ ├── cli.py # CLI interface
│ ├── loader.py # CSV parsing & grouping
│ ├── forecasting.py # Trend & MA forecasting
│ └── models.py # Data models
│
├── data/
│ └── gene_history_example.csv
│
├── README.md
└── pyproject.toml
Inside the project directory:
pip install -e .This registers the command:
mcgf
mcgf data/gene_history_example.csvmcgf data/gene_history_example.csv --gene MINT_UNBOUNDEDmcgf data/gene_history_example.csv --method ma --window 3mcgf data/gene_history_example.csv --horizon 3--------------------------------------------------------------------
MARKET CYCLE GENE FORECAST – 1 STEP AHEAD (method: trend)
--------------------------------------------------------------------
Total observations : 18
Total genes : 3
Gene Last Freq Forecast(+1)
--------------------------------------------------------------------
FEE_BASIC 0.320 0.365
FEE_HIGH_RISK_PATTERN 0.210 0.236
MINT_UNBOUNDED 0.120 0.138
--------------------------------------------------------------------
Done.
Professional. Clear. No Unicode issues. Designed for CMD, PowerShell, macOS, Linux.
Each row represents a gene’s presence in a given time step:
time_index,phase,gene,frequency
1,accumulation,MINT_UNBOUNDED,0.02
2,accumulation,MINT_UNBOUNDED,0.03
3,early_bull,MINT_UNBOUNDED,0.05
...
Where:
| Field | Description |
|---|---|
time_index |
numeric chronological step |
phase |
market phase label |
gene |
gene identifier |
frequency |
normalized frequency (0.0–1.0) |
Quantify how tokenomic genes mutate over time.
Predict the rise of dangerous patterns like:
- blacklist abuse
- dynamic tax scams
- honeypot structures
Identify which token species will re-emerge.
Anticipate upcoming tokenomic trends in new cycles.
MCGF is suitable for:
- evolutionary economics papers
- on-chain behavioral analysis
- DeFi risk studies
- system dynamics modeling
MIT License, open for research, modification, and commercial use.
Contributions, new forecasting models, dataset expansions, and research collaborations are welcomed.
If you are working on:
- tokenomics
- gene drift
- smart-contract behavioral evolution
- on-chain economics
- DeFi risk modeling
your insights are extremely valuable.