Deep-Learning Based Alpha Research
In markets overwhelmed by data, noise, and short-lived narratives, we identify small, persistent sources of outperformance that matter over time.
Producers & Merchants
Quant funds, quant departments and quant firms
Raw forecasts for your own alpha construction. For a given time period,
we predict the likelihood of different price growth areas and price drop areas — delivered as a private API.
Optimized hedging entries mean more revenue and more profit.
For Quant Experts
Producers & Merchants
Optimized hedging entries mean more revenue and more profit.
Raw forecasts for your own alpha construction. For a given time period, we predict the likelihood of different price growth areas and price drop areas — delivered as a private API.
The Product
We do not predict prices. For a defined time period, we predict the likelihood of different price growth areas and price drop areas.
You receive these raw probability distributions directly from our deep learning models, with full input transparency, and build your own policy and edge on top of them.
For every deep learning model you receive a complete input manifest showing which data feeds are going into the scenario output. Integrate directly into your quant stack, backtester, or execution layer.
The Problem
Professional trading desks and quant firms leave unrealised P&L on the table by relying on a single forecast narrative.
Alpha construction requires knowing the likelihood of different growth and drop areas across the full price distribution, not just a base case.
What you receive:
• Raw scenario outputs: bull, bear, base and tail paths
• Full input transparency per deep learning model
• Custom frequency and custom target forecasting periods
• Private API instance for maximum confidentiality
• Standardised infrastructure with no setup overhead
• Access to our team of quant experts
What you receive:
• Scenarios: bull, bear, base and tail paths
• Full input transparency per DL model
• Custom frequency and target forecasting
• Private API instance
• Infrastructure with no setup overhead
• Access to our team of quant experts
Standard forecasts do not account for:
• Multi-scenario price path distributions
• Regime shifts and structural breaks
• Tail events and volatility spike scenarios
Most alpha strategies therefore rely on:
• Static or generic price assumptions
• Single-model outputs with no scenario diversity
• Narrow narrative coverage that introduces structural bias
One scenario is never enough
A single forecast path creates blind spots. Our models generate the full scenario distribution so you can validate decisions, stress-test positions, and neutralise bias before it becomes risk.
01
Bull scenarios
Probability-weighted upside paths for defined time horizons, derived from momentum, flow and macro inputs.
Growth Path
02
Bear scenarios
Drawdown depth and velocity estimates, stress-tested against historical regimes and forward macro signals.
Drop Path
03
Tail and base scenarios
Base case and tail risk windows across the full distribution, with input attribution for every model output.
Full Distribution
Your private API instance
Unlike standard APIs, your requests are sent to a private API built exclusively for you. This ensures maximum confidentiality — your queries, positioning intentions and strategy remain entirely your own and are never shared with other clients.
Flow
Flow
Define your baseline and your policy
Identify an Alpha for your baseline
Client gets a report about different alpha performances and characteristics
Infrastructure Integration
Production and maintenance
Client Stories
Client Stories
A European wind power producer hedges its production using standard power futures.
Our signals identify whether the front-month contract or a contract further along the curve better reflects expected production and market conditions. The signal-based approach was tested against a systematic hedge that only matched time periods and volumes.
The result was a measurable improvement in hedge effectiveness and a reduction in residual risk.
A grain trading company hedges physical positions using exchange-traded futures.
Our models incorporate seasonality, forward curve structure, and liquidity dynamics to adjust hedge timing and contract selection.
Compared to a static hedging approach, the strategy improved hedge alignment with the physical exposure and reduced basis-driven volatility.
A European wind power producer hedges its production using standard power futures.
Our signals identify whether the front-month contract or a contract further along the curve better reflects expected production and market conditions. The signal-based approach was tested against a systematic hedge that only matched time periods and volumes.
The result was a measurable improvement in hedge effectiveness and a reduction in residual risk.
A grain trading company hedges physical positions using exchange-traded futures.
Our models incorporate seasonality, forward curve structure, and liquidity dynamics to adjust hedge timing and contract selection.
Compared to a static hedging approach, the strategy improved hedge alignment with the physical exposure and reduced basis-driven volatility.
Let us find the needles for you.
Contact us to unlock your alpha,
improve returns and protect revenue.
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Disclaimer: The information, analyses, research outputs, signals, forecasts, models, and other materials (collectively, the “Content”) provided by Needlestack Technologies Ltd. are for informational and research purposes only and are intended solely for use by professional investors, market participants, and other financially sophisticated users.