Automated Algorithmic Day Trading Platform
Budget: $1,500 – $3,000 USD
I need an automated algorithmic trading platform purpose-built for the stock market that can run three distinct styles—scalping, swing trading, and day trading—without manual intervention. The core of the system should ingest live market data, analyse it in real time, and execute orders automatically through my broker’s API while keeping latency to a minimum. Since this is a longterm project I am planning this project in 2 stages.
Once both stages are complete, it should do as much as possible of the data analytics on a desktop instead on any cloud versions as in later stages as I assume my own developed mathematical models will need a lot local computing power. For excessive computing power I already have the NVIDIA DGX spark that allows me to run local AI models. So also for the initial stage setup I need you to consider the later integration of the DGX spark.
The developer shall provide only Stage 1 with the aim of setting up the right infrastructure and deliver a functional platform with documentation. The platform shall be an automated AI based trading platform for stocks based on a VPS and allowing the data ingestion and trading engine working with AI based tools like Claude. The developer should consider broker connectivity (API), data ingestion, execution, research infrastructure, risk controls, AI/model infrastructure, monitoring, and deployment and provide a running solution with a user manual and description of the modules and interfaces.
I want to use the Charles Schwab banking and main activity area will the US stock market. So any VPS solution should be ideally US based.
An automated trading system (bot) developed in Python for Charles Schwab API. The bot will run on a VPS, will allow the user to input a list of stocks to trade during RTH. The bot will buy shares of each listed ticker once the price crosses a 100 EMA upwards on a closed 5-minute bar and will close the ticker position once the price closes below the EMA.
Stage 1 scope shall deliver:
1. API connection module
2. Market Data module
3. Trading engine
4. Reporting/logging module
The modular architecture shall allow us to extend or modify the modules during stage 2 and to replace the entry strategy with own system signals and computation running on the DGX spark.
Once both stages are complete, it should do as much as possible of the data analytics on a desktop instead on any cloud versions as in later stages as I assume my own developed mathematical models will need a lot local computing power. For excessive computing power I already have the NVIDIA DGX spark that allows me to run local AI models. So also for the initial stage setup I need you to consider the later integration of the DGX spark.
The developer shall provide only Stage 1 with the aim of setting up the right infrastructure and deliver a functional platform with documentation. The platform shall be an automated AI based trading platform for stocks based on a VPS and allowing the data ingestion and trading engine working with AI based tools like Claude. The developer should consider broker connectivity (API), data ingestion, execution, research infrastructure, risk controls, AI/model infrastructure, monitoring, and deployment and provide a running solution with a user manual and description of the modules and interfaces.
I want to use the Charles Schwab banking and main activity area will the US stock market. So any VPS solution should be ideally US based.
An automated trading system (bot) developed in Python for Charles Schwab API. The bot will run on a VPS, will allow the user to input a list of stocks to trade during RTH. The bot will buy shares of each listed ticker once the price crosses a 100 EMA upwards on a closed 5-minute bar and will close the ticker position once the price closes below the EMA.
Stage 1 scope shall deliver:
1. API connection module
2. Market Data module
3. Trading engine
4. Reporting/logging module
The modular architecture shall allow us to extend or modify the modules during stage 2 and to replace the entry strategy with own system signals and computation running on the DGX spark.