Software Development & Products

Overview

A neural network that combines quantitative and qualitative analysis to generate profitable trading signals. Unlike pure quant approaches, this system captures the "soft" factors that numbers alone miss.

Quantitative Inputs

• Financial ratios: P/E, ROE, EPS, revenue growth
• Economic indicators and market data
• Historical price and volume patterns
• Statistical models and backtested signals

Qualitative Inputs (Quantified)

• NLP sentiment scores from news and social media
• Management quality metrics derived from earnings calls
• Brand strength indicators
• Industry trend analysis
• Competitive advantage (moat) scoring

Approach

The system quantifies traditionally subjective factors—turning CEO interview tone, brand perception, and market sentiment into numerical features. These are combined with traditional financial metrics to train a neural network that can identify opportunities pure quant systems miss.

Status

Initial pipeline using Grok API performed well—successfully extracting sentiment signals from X posts and live news sources. Currently developing a new NLP pipeline to expand coverage and improve signal quality.

Overview

A neural network trained exclusively on price and volume data for market timing. Pure technical analysis approach—objective, rule-based, and fully backtestable.

Data Sources

• Historical price data (OHLCV)
• Trading volume patterns
• Price-derived indicators
• Chart patterns and formations

Technical Indicators

• Momentum: RSI, MACD, Stochastic
• Trend: Moving averages (SMA, EMA), ADX
• Volatility: Bollinger Bands, ATR
• Volume: OBV, Volume Profile
• Pattern recognition: Head & shoulders, triangles, trends

Approach

The system learns to identify market psychology and timing purely from price action. It ignores fundamentals entirely, focusing on what the market is actually doing rather than what it "should" do. This makes it objective and scalable.

Backtest Results

The AI strategy significantly outperforms SPY (S&P 500 ETF) buy-and-hold over the test period. SPY serves as the standard market benchmark—consistently beating it suggests the model is generating alpha (excess returns beyond passive investing).

Monte Carlo simulations show the AI strategy outperforms approximately 99.7% of random trading outcomes. This suggests the results aren't attributable to luck alone and may reflect genuine predictive edge.

Risk Considerations

While returns are promising, the strategy exhibits higher volatility than SPY buy-and-hold—sharper drawdowns and faster gains. This raises important questions about risk management and position sizing. A complete evaluation would require risk-adjusted metrics like Sharpe ratio (return per unit of risk).

Additionally, real-world factors such as transaction costs, slippage, taxes, and liquidity constraints are not fully captured in backtesting. Whether these results hold out-of-sample remains to be validated through live testing. Past performance is not indicative of future results.

Status

Backtesting complete with promising results. Currently developing the automated execution system for live trading validation.

Overview

An affordable ANPR (Automatic Number Plate Recognition) camera system built on Raspberry Pi hardware. Traditional ANPR solutions often cost thousands of dollars—our goal is to deliver comparable functionality at around $300 USD (~$500 NZD) per unit.

Core Features

• Real-time license plate detection and recognition
• Edge processing—no cloud dependency required
• Raspberry Pi-based for low cost and flexibility
• Optimized inference for embedded hardware

Target Applications

• Parking management and access control
• Small business security
• Private property monitoring
• Fleet and vehicle tracking

Ongoing R&D

Alongside ANPR, we're exploring additional detection capabilities for the platform—object recognition, pose estimation, and behavioral analysis. Potential applications include workplace safety monitoring (detecting phone use, unsafe postures, falls) and general security use cases. These features are in early research phases.

Status

The core software pipeline is largely complete. This project has been placed on hold while we focus on higher-priority initiatives. The technical foundation is solid and ready to resume when the time is right.

Hardware Roadmap

The working prototype runs on approximately $600 NZD (~$360 USD) in hardware, but profiling shows the current Raspberry Pi is significantly overpowered for the workload. This headroom opens a clear path to cost reduction—lower-spec processors can handle the inference pipeline comfortably, freeing up budget for better optics or environmental hardening.

If demand warrants, we're exploring custom board designs to streamline the hardware stack further, potentially pushing unit costs well below our $300 USD (~$500 NZD) target.

Overview

Appeal Labs is our in-house mobile and web app development studio, operating as a trading name under JP Dev. We take ideas from concept to launch—whether they're our own, a client's, or a collaborative venture with the right partner.

What We Do

• Own Products — Building and launching our own B2B and B2C applications
• Client Development — Full-service app development for businesses and individuals
• Creator Partnerships — Collaborating with influencers and content creators on shared ventures

Partnership Model

We work with creators, influencers, and entrepreneurs who have audiences but need technical expertise. The structure is simple:

• Appeal Labs handles: All development, maintenance, hosting, and technical infrastructure
• Shared work: Design direction, theming, content strategy—collaborative decisions
• Partner handles: Marketing, promotion, and audience engagement
• Revenue: Profit-sharing arrangement tailored to each project

This model lets creators launch apps without upfront development costs or technical overhead, while we gain access to established audiences and distribution channels.

Status

Active and open to new projects. If you have an app idea or an audience looking for a product, get in touch.