Engineering Smart Systems for Smart Markets.
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Welcome
Welcome
We build custom software solutions with deep expertise in the finance sector. Our team also delivers specialized AI model training and deployment, empowering clients to automate workflows, extract actionable insights, and make more informed decisions.
Projects
Real-Time Market Bias Engine
Real-Time Market Bias Engine
Every second, global markets generate millions of microscopic data points — orders placed, cancelled, modified, executed. Hidden within this chaos lies structure, intent, and opportunity.
Our system captures and analyzes thousands of market depth events per second, reconstructing the true heartbeat of the order book in real time.
Unlike conventional analytics tools that rely on aggregated candles or delayed feeds, our engine operates directly at the microstructure layer — the raw depth-of-book data — decoding the continuous tug-of-war between liquidity providers and aggressive participants.
Each event is processed through a distributed pipeline of asynchronous collectors, adaptive smoothers, and bias estimators, building a live multidimensional model of:
Order-flow aggression and liquidity imbalance
Market-maker defense and absorption patterns
Synthetic COT (Commitment of Traders) pressure curves
Virtual bias trajectories that anticipate directional pivots before price confirms
The result is a living system that doesn’t just observe the market — it interprets its intent.
By blending microsecond event analytics with advanced smoothing algorithms and virtual bias modeling, we reveal hidden shifts in conviction long before they surface on the chart.
This is market intelligence at machine speed — a fusion of quantitative engineering, algorithmic intuition, and raw computational power — designed for those who trade on signal, not noise.
Financial Market RAG System Trained on 800 Expert Volumes
Financial Market RAG System Trained on 800 Expert Volumes
This system is designed to provide accurate, context-aware answers to financial questions by leveraging a Retrieval-Augmented Generation (RAG) architecture. It was built using a curated dataset of 800 financial books covering topics such as investment strategies, risk management, market structures, and economic theory.
This architecture combines the interpretability of search with the fluency of generative AI, making it ideal for financial research, education, and decision support.
Case Analyses
Case Analyses
Auction Market Theory in Algorithmic Trading
Auction Market Theory in Algorithmic Trading
Auction Market Theory in Proprietary Trading
Auction Market Theory in Proprietary Trading
At Serialcoder, we leverage Auction Market Theory (AMT) as a core framework in our proprietary trading systems. AMT views financial markets as dynamic auctions where price is continually negotiated between buyers and sellers. It emphasizes the interplay of price, time, and volume — a perspective that aligns naturally with our quantitative approach.
Central to AMT is the market profile, which reveals where trading activity clusters over time. This allows us to pinpoint critical areas such as the Point of Control (POC), value area, and high/low volume nodes — levels that indicate consensus, rejection, or imbalance in the market.
Our proprietary systems ingest tick-level data, volume profiles, and order flow, extracting features such as:
Zones of volume acceptance and rejection
Shifts in value areas across sessions
Time-based anomalies in volume distribution
These insights feed directly into our in-house trading models. We use this data to:
Identify auction imbalances and transitions in real time
Trigger trade entries based on structural context, not noise
Continuously adapt to changes in market participation and behavior
By embedding AMT principles into our algorithmic infrastructure, we align with the market’s underlying mechanics — improving precision, responsiveness, and edge in live trading environments.
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