PROJECT

Algorithmic Trading Software for Controlled Automated Trading

Custom software development, Real-time market data, Trading strategy development, Backtesting and optimization, Automated order execution, Risk monitoring, Trading reports

  • Industry: Investment management
  • Bulgaria Location: Bulgaria
  • Delivery time: 16 weeks
  • Technologies: Python, JavaScript / TypeScript (Node.js)
4+ months

This client is an investment firm from Bulgaria, and manual market decisions are slow and inconsistent. The requested software takes real-time or historical market data and applies configurable trading strategies. It returns signals, test results, and automated orders for the firm’s trading team. Reports keep the results available for review, bringing strategy development, analysis, and execution into one controlled system.

Summary

Problem

The investment firm spends time moving between market analysis, strategy testing, order execution, and reporting.

Solution

The solution became one trading system for configurable strategies, market data, and automated orders. It also includes risk controls and reports.

Result

The software is made to reduce manual trading work and repeated checks between trading steps.

Client story

The client is an investment firm from Bulgaria. It works with market data and trading strategies that need analysis, testing, execution, and monitoring.

Manual and inconsistent decisions cost the firm time. Its trading team also needs to review signals, positions, and orders.

The request was simple: create an automated trading system that accepts real-time or historical data, applies client-defined rules, generates trading decisions, and manages orders through the required trading connection.

The solution was designed around separate modules for strategy configuration, live signals, backtesting, execution, and reporting. Risk monitoring was included as a separate part of the trading process.

The client asked for

  1. Configure strategies with indicators, rules, machine learning, or price action
  2. Use real-time or historical market and related data
  3. Generate signals, entry and exit points, and trading decisions
  4. Execute and manage orders through broker or exchange connections

Why ready-made software did not fit

Brokerage platforms cover buying, selling, and portfolio management, but they do not match this firm’s complete process. The requested solution was designed to combine custom strategies, backtesting, and automated execution in one workflow. Risk controls and live monitoring were included for the firm’s trading process.

Project overview

The solution was designed around a strategy editor where the firm configures trading rules, indicators, and market parameters. Historical or real-time data feeds the same strategy setup.

To handle strategy development, a backtesting module was provided. It compares strategy behavior on historical data and shows trade results and performance metrics.

The live dashboard analyzes market data and displays buy and sell signals, entry and exit points, and open positions. This gives the trading team one view of current market activity.

Automated execution was designed around trading platforms, broker APIs, and exchange APIs. The execution monitor shows order status, executed trades, and risk limits while orders are processed.

Trade history, P&L, and maximum drawdown are available for review. The system exports these records to JSON, Excel, or PDF.

Key features delivered

  • Configurable trading strategy editor
  • Real-time and historical market data
  • Technical-indicator and rule configuration
  • Buy and sell signal generation
  • Backtesting and strategy optimization
  • Automated order execution
  • Risk-limit and execution monitoring
  • Open-position and performance tracking
  • Trade logs and reporting
  • P&L, JSON, Excel, and PDF exports

Can be added

+ News and social-media analysis + Company filings and fundamental data + Machine-learning strategies + Forward or paper testing + TradingView alerts to Telegram + Additional broker or exchange connections

Who else can use this solution

These businesses have a similar task, and the same solution can be adapted to their trading data, strategies, and execution rules.

A stock and index trading business

Task It needs automated intraday or day-trading decisions from market data.
How the solution helps The solution applies its trading rules, generates signals, and monitors positions and orders.

An Indian-market trading operation

Task It trades equities, options, futures, or indices through broker platforms.
How the solution helps The solution connects market data and broker execution with strategy testing, signals, and risk controls.

A quantitative trading operation

Task It develops statistical, machine-learning, or high-frequency strategies.
How the solution helps The solution provides configurable algorithms, backtesting, optimization, execution, and performance reporting.

Technology stack

Python

Strategy and data processing

Python supports market-data analysis, strategy logic, testing, and reporting.

JavaScript / TypeScript (Node.js)

Trading interface and services

JavaScript and TypeScript support the strategy interface and shared trading services.

ccxt

Exchange API connection

ccxt (a library that connects trading software to exchanges) provides exchange connections for automated trading.

vectorbt

Large-scale strategy backtesting

vectorbt (a library for testing trading strategies) analyzes strategy behavior on historical data.

@tradejs/core

Strategy authoring and helpers

@tradejs/core (a browser-safe trading and backtesting library) supports strategy configuration and shared trading functions.

pandas

Market data analysis

pandas provides data structures for market-data analysis and trading results.

numpy

Array calculations

numpy provides array calculations for market and performance data.

scipy

Scientific computing

scipy provides fundamental algorithms for scientific computing in Python.

Excel, JSON and PDF

Report export formats

These formats carry P&L, trade logs, backtest results, and reports out of the system.

Integrations

Broker APIs and exchange APIs

The system sends and manages automated orders through the required broker or exchange connection.

Trading platforms

Trading platforms provide the market and execution connection used by the trading system.

NSE and BSE

The nse and bse Python APIs provide access to Indian stock-market data where required.

Polygon.io

The polygon Python wrapper provides market data for equities, bonds, options, and related research.

Excel, JSON and PDF

The system exports trade history, P&L, drawdown, backtest results, and reports in these formats.

The result

Strategies stay connected to market data

The strategy configuration screen brings market selection, indicators, and rules together. The firm configures the strategy before testing or live analysis.

The trading engine applies the configured rules to historical or real-time market data and prepares the strategy for signal generation.

How it works

  1. The strategy editor stores trading strategy parameters and client-supplied rules.
  2. Market data supplies the selected instrument and timeframe.
  3. The indicator and rule modules prepare the strategy for backtesting or live analysis.
  • Technical indicators, price action, chart patterns, sentiment, and machine learning can be included where required.
Tip: The strategy setup can be structured around the firm’s own market, asset type, timeframe, and execution rules.
Strategy and market configuration

Strategy and market configuration

Functions on this screen
Develop Trading Algorithms Analyze technical indicators Backtest Trading Strategy

Live signals are visible in one trading view

The live market dashboard shows current market data, generated signals, and entry and exit points. Technical analysis and open positions remain visible beside the active market view.

This gives the firm a clear place to review automated trading decisions before execution and while positions remain open.

How it works

  1. The live market view displays real-time market data and current trading signals.
  2. The technical-analysis panel shows indicator information with entry and exit points.
  3. The dashboard summarizes open positions and provides alert information.
Live market and signal dashboard

Live market and signal dashboard

Functions on this screen
Utilize Real-Time Data Feeds Real-time Market Analysis Analyze technical indicators Generate trading signals Display trading data

Strategies are tested before execution decisions

The backtest and optimization screen compares strategy behavior on historical data. It shows trade-by-trade results and performance charts.

Optimization controls help the trading team review how strategy parameters affect P&L and maximum drawdown before using the strategy with live data.

How it works

  1. The historical test-period selector provides the period and market data for testing.
  2. The backtesting module calculates strategy results, trades, and P&L.
  3. The optimization module compares parameter results and displays them in tables and charts.
  • Backtesting can use vectorbt (a library for testing trading strategies), backtesting, bt, or @tradejs/core.
Note: Historical results describe strategy behavior on the selected data; they do not establish future trading performance.
Backtest and optimization results

Backtest and optimization results

Functions on this screen
Backtest Trading Strategy Trading Algorithm Optimization Calculate maximum drawdown

Orders and risk controls remain observable

The execution monitor brings order status, risk limits, and execution logs into one screen. It covers executed, modified, and canceled orders as well as current trading activity.

Automated execution connects the configured strategy with trading platforms, broker APIs, or exchange APIs while the monitor keeps the order path visible.

How it works

  1. The execution module sends and manages orders through the configured trading connection.
  2. The order-status table displays executed, modified, and canceled orders.
  3. The monitoring modules show risk limits, live logs, and performance metrics.
Orders, risk, and execution monitor

Orders, risk, and execution monitor

Functions on this screen
Real-Time Trade Execution Execute Market Orders Risk management monitoring

Trading records are ready for review and export

The reports and trade history screen organizes executed trades, trade logs, and P&L. Analysts, managers, and accountants can review the same trading record.

Export actions provide structured information for further analysis and financial reporting. The system writes the selected information to JSON, Excel, or PDF.

How it works

  1. The report selector loads trade history, executed trades, and P&L information.
  2. The reporting module creates backtest and real-time reports from trading records.
  3. The export modules write the selected information to JSON, Excel, or PDF.
  • pyexcel-io (a library for reading and writing spreadsheet data) supports structured tabular exports.
  • jspdf (a JavaScript library for creating PDF documents) supports PDF report generation.
Reports and trade history

Reports and trade history

Functions on this screen
Generate reports for analysis P&L Export to Excel Calculate maximum drawdown Log Trading Activity

What the solution gives

With this solution, the investment firm can:

  • Configure trading strategies around its own indicators, rules, markets, and timeframes
  • Analyze real-time and historical market data in one system
  • Generate signals, entry and exit points, and trading decisions
  • Test and optimize strategies before live execution
  • Manage automated orders through broker, exchange, or platform connections
  • Monitor risk, positions, logs, performance, and reports

Could ready-made systems do the job?

Before the design, the task was compared with ready-made products by their own descriptions.

What the client needs Brokerage platforms Kite, MetaTrader 5, thinkorswim Automated trading tools Galileo FX, Abbado Custom solution
Configure custom trading strategies partly —
Use live or historical market data partly
Generate signals and trading decisions partly
Execute orders through APIs partly
Fits the client's own rules: stages, roles, reports — —
All steps in one place — —
No manual steps between programs — —

By the public descriptions of the products and their category.

Ready-made products cover important parts of the task, such as trading, analysis, and order execution. A ready-made product is enough when only one of these parts is needed.

This client needed its own software because the required strategies, testing, risk controls, monitoring, reports, and execution rules had to work together in one place.

In the end

In the end, the solution was made to take manual work out of the path from market data and strategy rules to tested decisions, controlled orders, and trading reports.

FAQ

Thinking about building a similar system? Read the answers below to see how algorithmic trading software can be shaped around market data, strategies, execution, and reporting.

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