Protocol_v1.0.4

Indicators Guide

FASTJ Predict™ Engine • Methodology & Transparency

01// OUR PHILOSOPHY

The Signal Tower does not provide real marketplace transactional data. Instead, it offers forward-looking market signals generated by mathematical simulation models (FASTJ Predict™ Engine). These signals are designed to extract multi-dimensional momentum and volatility patterns, providing a level of stochastic insight traditionally reserved for high-frequency institutional frameworks.

We are not claiming to show absolute truth. All indicators are synthetic or semi-synthetic. Any deviation from actual market conditions stems from model limitations and our current mathematical capabilities. We openly document our approach below and continuously iterate to improve.

02// CORE INDICATORS

Ticker Tape

A flowing feed of highlighted items with dynamic index values. It reflects real-time popularity trends and price/weight adjustment suggestions for individual products across various platforms (TikTok, Shopee, AliExpress, etc.), ensuring the stream prioritizes high-momentum items while correcting for short-term speculative noise.

Multiplier_{t} = 1.0 + \kappa (\theta - S_{t-1}) + Shock_t

High-Precision Simulation

Logic Kernel:

Harmonic simulation with category- and tag-aware amplitude adjustment

Sentiment Index (Greed & Fear)

A composite gauge reflecting overall market sentiment. It synthesizes multiple factors including platform activity and simulated seller behavior into a normalized 0-100 score.

Sent = 50 + (\alpha \cdot \frac{\sum \Delta Trend}{N}) \cdot \log(1 + Vel)

High-Precision Simulation

Logic Kernel:

Multi-factor weighted synthesis with smoothing mechanisms

Market Pulse

Provides a macro-level view of platform vitality by aggregating category baselines and seasonal biases.

Pulse_{t} = \sum (\frac{Price \cdot \sigma}{Base}) \cdot w_i

High-Precision Simulation

Logic Kernel:

Volume-weighted Dynamic Category Baseline with Adaptive Bayesian Volatility Bias

Market Heat Ranking

Visual “traffic light” system indicating relative competitiveness and compliance risk across platforms.

Heat_{p} = \frac{Base \cdot Bias}{\max(Heat_{all})} \cdot 100

High-Precision Simulation

Logic Kernel:

Baseline heat adjusted by seasonal factors, trending signals, and evidence-based Bayesian refinement

FastJ Predict™ Forecast

The core predictive engine for simulating future 72-hour price and demand paths. By executing 1,000 Monte Carlo path simulations, it identifies the 50th percentile as the median forecast value while accounting for market unpredictability.

S_{t+\Delta t} = S_t \cdot \exp \left( (\mu - \frac{\sigma^2}{2})\Delta t + \sigma\sqrt{\Delta t} \cdot \epsilon \right)

High-Precision Simulation

Logic Kernel:

High-fidelity Monte Carlo Pathing based on Discrete Geometric Brownian Motion (d-GBM)

GEN_1

Current Model Stage & Our Commitment

Our current models are in the first-generation harmonic resonance stage. We are fully aware that real market dynamics are far more complex than any single statistical or simulation approach.

Therefore, we openly acknowledge the limitations of the current algorithms, particularly in handling sudden non-linear events (such as platform policy shifts or unexpected external shocks). Our team — consisting of product managers and data engineers — is actively working on the second-generation engine, aiming to introduce deeper Bayesian posterior refinement and improved adaptive mechanisms in the near future.

We believe transparency builds trust. Any inaccuracy you observe is primarily the result of model constraints and our ongoing mathematical refinement process.

Technical Disclaimer

All predictions generated by the FASTJ Predict™ Engine are strictly for reference and informational purposes. They do not constitute financial, investment, or commercial advice. Actual marketplace performance may deviate — potentially substantially — from our simulated signals. Users assume full liability for any business decisions; FASTJ Studio bears no responsibility for market outcomes or data variance.