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Engineering Smart Systems for Smart Markets.

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

EmberGen

Riding the Wave of AI-Generated Images

AI-generated imagery has exploded in popularity thanks to breakthroughs in diffusion models and generative adversarial networks (GANs). From hyper-realistic portraits to imaginative concept art, individuals and businesses are embracing AI tools to create content faster, cheaper, and at scale — without needing design skills.

This technology is transforming industries:

Marketing teams use it to generate custom visuals for campaigns.
E-commerce brands build entire product shoots virtually.
Entertainment and gaming companies rapidly prototype characters and environments.
Finance and tech use visuals to explain complex data or brand AI-native tools.

How We Cater at Serialcoder

At Serialcoder, we help clients tap into this creative revolution through:

Custom-trained AI models fine-tuned on domain-specific imagery (e.g., finance, healthcare, fashion).
Image generation APIs and backends that plug into your apps, dashboards, or workflows.
Workflow automation, so visuals can be generated on demand based on input (text, form data, prompts).
Brand-safe guardrails, ensuring outputs stay on-message and style-consistent.

Whether you're prototyping designs, generating branded content, or integrating AI art into a product — we build the infrastructure to make it yours.

Go take a look https://ember-gen.com

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

Auction Market Theory in Algorithmic 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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