Automated stock market analytics and algorithmic trading

Three independent scenarios illustrate different tasks: A — opening a trade on a combined signal; B — blocking a trade because of a corporate event; C — strategy development, long-term backtesting and ML.

Example: an automated stock market system

Imagine a trading team analysing several thousand stocks and exchange-traded funds. Market Data API provides OHLCV prices, volumes and other available data. Separate sources supply fundamental indicators, corporate events and macroeconomic releases.

Python processes check information quality and freshness, align time intervals and store data in PostgreSQL. The technical module calculates indicators and identifies defined patterns. ML models analyse market features, while an LLM can extract facts from financial documents and news.

Trading Strategy combines the results according to defined rules and produces a decision: buy, sell, adjust a position or do nothing. A preliminary signal is not sent to the broker automatically: Risk Management checks capital, limits, exposure and event-based restrictions. If permitted, Broker API receives the order; positions and the log are updated after the broker confirms it.

A / Combined signal and execution

The technical module detects a chart pattern in the illustrative stock stock-A, and the analytical model confirms an acceptable time and price range. The strategy produces a preliminary BUY signal. The current price of $103 has not yet reached the planned $104 entry, so the system checks risk and waits.

Position size is calculated from the distance to the protective level: $104 − $99 = $5 per share; $50,000 × 0.5% = $250; $250 / $5 = 50 shares. The notional value is $5,200 (10.4% of the account without leverage).

01 Market Data API delivers prices; the system calculates indicators and recognises the pattern.

02 The model checks additional trading conditions; Trading Strategy produces a preliminary BUY.

03 Risk Management calculates 50 shares and checks available buying power, the portfolio and restrictions.

04 The trading module waits for $104, monitors signal validity and rechecks risk before execution.

05 Once the level is reached, a Broker API order is created; the example shows Submitted → Filled.

06 PostgreSQL records the fill, the 50-share position and the committed $5,200.

RESULT A A purchase of 50 stock-A shares at $104 is filled; notional value $5,200; planned risk $250.

The actual execution price, slippage, commissions and a protective order potentially filling at a worse price can change the actual outcome and risk. These factors must be included in historical strategy testing.

B / Event-based restriction and trade blocking

The technical strategy has already produced a BUY signal. In parallel, the news component receives a significant negative corporate event and verifies the source and the issuer. Under the strategy rules, this class of event prevents opening a new long position.

01 Market data produces a preliminary BUY signal.

02 News / Corporate Events confirms a priority negative event for the same instrument.

03 Risk Management receives a restriction with higher priority than the technical signal.

04 Trade permission is revoked; Broker API is not called.

05 The system records the reason and continues monitoring.

RESULT B Trade Blocked: a BUY signal was received, no new position was opened and no order was submitted to the broker.

The example illustrates the priority of risk management rules: a sound trading algorithm must identify signals and reject prohibited trades.

C / Strategy research and ML

An analyst proposes a strategy based on technical indicators: over the last 6 months it delivered an annualised return of about 55%. The figure looks attractive, but a short history does not establish robustness.

Stage 1 — Long-term historical testing

Across the 1996–2025 historical period, the baseline strategy periodically produces high profits. However, alternating strong and weak periods leave a negative total result. The six-month success is not supported by the longer history.

Stage 2 — Researching weaknesses

The analyst investigates the market regimes in which the strategy loses effectiveness. Additional hypotheses are formed around trend, volatility, trading volumes, price patterns, momentum and other available factors. An ML model helps assess these features but does not guarantee improvement.

Stage 3 — Training, validation and testing

The configuration is locked after validation. In the example, the modified strategy achieves a CAGR of 25% and maximum drawdown of 10% over 2016–2025.

Methodological limitation: the baseline strategy was previously studied across the full 1996–2025 period. Consequently, 2016–2025 cannot be considered fully independent of the overall research process. Additional validation requires new data, walk-forward testing or subsequent Paper Trading.

Stage 4 — Robustness testing

The next step is to simulate realistic commissions, spreads, slippage, liquidity and execution constraints. Following stress tests and other checks, the strategy can move to Paper Trading. Launching it on a live account requires a separate decision.

RESULT C Initial idea → 30-year backtest with a negative outcome → new hypotheses → ML → illustrative CAGR 25% and MDD 10% → additional validation.

An automated trading architecture comprises five functional components. Each has its own task and interacts with the others through defined interfaces and events.

Component 1 — Data Loader / Data ingestion

Receives prices, trading volumes, fundamental indicators, corporate news and macroeconomic information from external sources. Performs initial quality checks, normalises data and stores it in PostgreSQL. Prepared information is passed to the relevant analytical components.

Component 2 — Market Analysis / Market data analysis

Processes prices, technical indicators, chart patterns, market trends and other quantitative features. Uses statistical methods, software algorithms and ML models where appropriate. Produces trading signals and conditions for opening or closing positions.

Component 3 — Event Analysis

Analyses corporate news, financial statements, economic releases and other external events. Uses an LLM where needed to extract and classify information. Produces additional signals, restrictions and priority events that can confirm or block the strategy's actions.

Component 4 — Risk Management

Receives the outputs of both analytical components and checks whether a trade is permitted. Controls position sizes, available funds, portfolio limits, market restrictions and blocking events. Allows execution only when all mandatory conditions are met. New restrictions can block a prepared order before it is submitted.

Component 5 — Execution Module / Order execution

Receives permitted trading instructions, prepares and submits orders through Broker API, and monitors confirmations, partial fills, cancellations and rejections. Updates positions, account state and the transaction log. Latency-critical operations can use a high-performance implementation in C, C++ or C#.

Strategy research and development

A separate research process combines historical analysis, backtesting, ML model development, validation and Paper Trading. It creates, tests and improves algorithms that can subsequently be used in the analytical components of the live system.

01 / Collecting and preparing market data

The pipeline connects to Market Data API, fundamental data providers, corporate calendars and news sources. Python performs ETL/Data Quality: checking gaps, timestamps, corporate actions, price validity and source freshness. PostgreSQL stores history for research and trading calculations.

02 / Technical, chart and fundamental analysis

Modules calculate PSAR, SMA, RSI, MACD, ATR, volume and volatility features, identify defined patterns and evaluate strategy conditions. Fundamental analysis uses financial statements, cash flows, earnings, debt levels and macroeconomic indicators. Final signals are produced by verifiable rules.

03 / Event and AI analysis

A separate pipeline receives news and corporate events. An LLM extracts entities, facts, dates and metrics; software checks verify sources and match events to instruments. Depending on strategy rules, events can inform the analyst, restrict position size or block entry.

04 / ML and strategy development

ML can classify market regimes, estimate the probability of defined events and identify patterns in market features. Research must prevent look-ahead bias, data leakage, survivorship bias and over-optimisation. Machine learning can discover additional patterns in market data and improve trading strategies. ML models are evaluated against baseline algorithms on historical data, including independent test periods. This helps determine whether ML actually improves returns, reduces risk and makes the strategy more robust.

05 / Backtesting and Paper Trading

Historical simulation reproduces signals and orders and calculates returns, CAGR, maximum drawdown and risk metrics. It accounts for commissions, spread, slippage and potential liquidity constraints. Paper Trading provides additional testing on new data without risking real capital.

06 / Signals and Risk Management

The trading strategy determines entry, exit and position-size parameters. Independent Risk Management checks capital, buying power, portfolio constraints, liquidity and event-based restrictions. A final check is repeated immediately before submission through Broker API.

07 / Broker API and execution

The broker returns order statuses: accepted, filled, partially filled, rejected or cancelled. The system tracks orders, positions, funds and checks. Duplicate prevention, broker reconciliation, logging and an emergency stop are part of a reliable trading workflow.

08 / Monitoring and performance control

PostgreSQL records signals, checks, orders, fills and reasons for rejection. BI displays results and deviations from expectations. Notifications help identify technical and market risks. New strategy versions are tested before deployment.

A comprehensive automated system can analyse many financial instruments simultaneously, process streams of prices, fundamental data and corporate events, generate trading signals and manage order execution under consistent rules. All analytical outputs, trading decisions, risk checks and operations are recorded for subsequent review and analysis.

The architecture combines five connected functional components. The first continuously ingests and prepares market data. Two independent analytical components process price information and external events, producing trading signals and additional decision conditions. The fourth controls risks, restrictions and the permissibility of operations. The fifth submits authorised orders through Broker API, tracks execution and updates the trading system's state.

A separate development track covers trading hypotheses, historical testing, machine learning and strategy robustness. Prepared algorithms undergo the necessary checks before deployment into live analytical components.

The main outcome is a single automated infrastructure in which information collection, analysis, trading decisions, risk management and execution work together consistently. The system does not guarantee profits. It provides reproducible algorithms, adherence to defined constraints and the ability to reject a trade automatically when prohibited conditions arise.

RULE Data → Analytics → Strategy research → Backtesting → Risk control → Execution → Monitoring