Automated Trading.ipynb

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    "# Automated Trading \n",
    "With the advent of automated trading, the trading scenario changed or rather evolved into a more accurate, time-saving, and faster approach. In its basic form, we can portray the transfer of data from the exchange and the automated trading system as follows:\n",
    "\n",
    "![alt text](https://d1rwhvwstyk9gu.cloudfront.net/2023/01/Algo-trading-system.jpeg)\n",
    "\n",
    "The entire automated trading system can now be broken down into -\n",
    "- The exchange(s) – the external world\n",
    "- The server\n",
    "- Receives market data \n",
    "- Stores the market data\n",
    "- Store orders generated by the user\n",
    "\n",
    "**Application**\n",
    "- Takes inputs from the user initially for the decisions such as stop loss, limits, the preferred financial instrument for trading etc.\n",
    "- Acts as an interface for viewing the information including the data and orders\n",
    "- Acts as an order manager sending orders to the exchange\n"
   ]
  },
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   "source": [
    "# Difference between Automated Trading and Alogorithmic Trading \n",
    "\n",
    "Algorithmic trading just means that the trading strategy follows an algorithm and thus can be backtested with historical data. It can, however, still be executed manually by entering orders by hand. Automated trading takes this a step further, and even executions are completely automated.\n"
   ]
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    "# Automated Trading Architectures\n",
    "Automated trading architectures, also known as algorithmic trading systems or automated trading systems (ATS), refer to the infrastructure and frameworks used to execute trades in financial markets automatically based on pre-defined rules and algorithms. These architectures typically involve several components working together to analyze market data, make trading decisions, and execute orders without human intervention.\n",
    "\n",
    "## Common Components And Architectures\n",
    "![alt text](https://d1rwhvwstyk9gu.cloudfront.net/2023/01/Architecture.jpg)\n",
    "### Market Data Feed\n",
    "This component collects real-time or historical market data from various sources such as stock exchanges, financial news websites, and data vendors. It could include price data, volume data, order book data, and other relevant information.\n",
    "\n",
    "### strategy Development Environment\n",
    "Traders or quantitative analysts use this environment to develop, test, and optimize trading strategies. It often includes backtesting capabilities to assess the performance of strategies using historical data.\n",
    "### Algorithmic Engine \n",
    "The algorithmic engine is the core of the automated trading system. It contains the algorithms that analyze market data, generate trading signals, and determine the timing and size of trades according to predefined rules.\n",
    "### Risk Management Module\n",
    "This module is responsible for monitoring and managing the risk associated with trades executed by the algorithmic engine. It may include controls to limit position sizes, set stop-loss orders, and monitor overall portfolio risk.\n",
    "### Order Management System (OMS)\n",
    "The OMS receives trade orders generated by the algorithmic engine and routes them to the appropriate trading venues such as stock exchanges or alternative trading systems. It also manages order execution, order routing, and trade reporting.\n",
    "### Execution Management System (EMS)\n",
    "The EMS is responsible for executing trades efficiently and at the best available prices. It may include algorithms for order routing, smart order routing to multiple venues, and tools for minimizing market impact.\n",
    "### Backtesting and Simulation Tools\n",
    "These tools allow traders to backtest trading strategies using historical market data to assess their performance before deploying them in live trading environments. They help validate the effectiveness of strategies and identify potential pitfalls.\n",
    "### Monitoring and Reporting Tools\n",
    "These tools provide real-time monitoring of the automated trading system's performance, including metrics such as trade execution speed, fill rates, and profitability. They also generate reports for compliance and auditing purposes.\n",
    "### Infrastructure and Connectivity\n",
    "Reliable infrastructure and high-speed connectivity are essential for automated trading systems to operate effectively. This includes servers, networks, and data centers located close to trading venues to minimize latency.\n",
    "### Compliance and Regulatory Controls\n",
    "Automated trading systems must comply with regulatory requirements and industry standards. Compliance features may include pre-trade risk checks, transaction reporting, and audit trails to ensure transparency and accountability.\n"
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    "# system architecture of a algorithmic trading platform \n",
    "![alt text](https://d1rwhvwstyk9gu.cloudfront.net/2023/01/System-architecture--1-.jpeg)\n",
    "## Market Adapter\n",
    "Exchange or any market data vendor sends data in their own format. Your algorithmic trading system may or may not understand that language. Exchange provides you with an API or an Application Program Interface ⁽²⁾ which allows you to program and create your own adapter which can convert the format of the data into a format that your system can understand.\n",
    "## Complex Event Processing Engine\n",
    "A complex event is a set of other events that together implies an occurrence of something of significance. These include stock trends, market movements, news etc. Complex event processing is performing computational operations on complex events in a short time.\n",
    "\n",
    "In an automated trading system, the operations can include detecting complex patterns, building correlations, and relationships such as causality and timing between any incoming events.CEP systems process events in real-time, thus the faster the processing of events, the better a CEP system is. \n",
    "\n",
    "The CEP system comprises of four parts:\n",
    "\n",
    "- CEP engine\n",
    "- CEP rules\n",
    "- CEP WS\n",
    "- CEP result interface\n",
    "![alt text](https://d1rwhvwstyk9gu.cloudfront.net/2023/01/CEP.jpg)\n",
    "The two primary components of any CEP system are the CEP engine and the set of CEP rules. The CEP engine processes incoming events based on CEP rules. These rules and the events that go as input to the CEP engine are determined by the trading system (trading strategy) applied.\n",
    "\n",
    "The majority of work is concentrated in CEP system block, formulating trading strategies; performing rigorous backtesting, optimization, and position-sizing among other things.\n",
    "\n",
    "This is done to ensure the viability of the trading strategy in real markets. No single strategy can guarantee everlasting profits. Hence, quants are required to come up with new strategies on a regular basis to maintain an edge in the markets.\n",
    "\n",
    "There are a number of popular automated trading systems that are classified as different strategies namely momentum strategies, statistical arbitrage, market making, etc.\n",
    "\n",
    "## Order Routing System\n",
    "\n",
    "The order is encrypted in the language that the exchange can understand, using the APIs which are provided by the exchange. There are two kinds of APIs provided by the exchange: native API and FIX API. Native APIs are those which are specific to a certain exchange.\n",
    "\n",
    "![alt text](https://d1rwhvwstyk9gu.cloudfront.net/2023/01/Order-manager.jpg)\n",
    "\n",
    "The FIX (Financial Information Exchange) protocol is a set of rules used across different exchanges to make the data flow in security markets easier and more effective. \n",
    "\n",
    "One can send orders through the automated trading system to exchanges or non-exchanges, and ORP should be able to handle orders to different destinations.\n",
    "\n",
    " the order signal can either be executed manually by an individual or in an automated way. The latter part is what we consider an “Automated trading system”. The order manager module comprises different execution strategies which execute the buy/sell orders based on pre-defined logic.\n",
    "\n",
    "Some of the popular execution strategies include VWAP, TWAP, etc. There are different processes like order routing, order encoding, transmission, etc. that form part of this module. \n"
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    "# Risk management in Automated Trading Systems\n",
    "Since automated trading systems work without any human intervention, it becomes pertinent to have thorough risk checks to ensure that the trading systems perform as designed. The absence of risk checks or faulty risk management can lead to enormous irrecoverable losses for a quantitative firm. Thus, a risk management system (RMS) forms a very critical component of any automated trading system.\n",
    "\n",
    "There are 2 places where risk management is handled in automated trading systems:\n",
    "\n",
    "- Within the application – We need to ensure those wrong parameters are not set by the trader. It should not allow a trader to set grossly incorrect values or any fat-finger errors.\n",
    "- Before generating an order in the order management system (OMS) – Before the order flows out of the system we need to make sure it goes through some risk management system. This is where the most critical risk management check happens.\n",
    "\n",
    "\n",
    "\n"
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    "# Automated trading system protocols\n",
    "![alt text](https://d1rwhvwstyk9gu.cloudfront.net/2023/01/Automated-trading-system-protocols.jpg)\n",
    "Automated trading systems rely on various protocols and standards to ensure efficient communication, data exchange, and interoperability between different components and external systems. Here are some common protocols used in automated trading systems:\n",
    "\n",
    "### FIX Protocol (Financial Information eXchange)\n",
    "FIX is one of the most widely used protocols in electronic trading, particularly in the equity, fixed income, and foreign exchange markets. It defines a standard format for transmitting trading-related messages between counterparties, including order placement, execution, and trade allocation. FIX messages can be sent over different transport protocols such as TCP/IP and are typically used for communication between trading systems, brokers, exchanges, and other financial institutions.\n",
    "### OMS/EMS Protocols\n",
    "Many order management systems (OMS) and execution management systems (EMS) use proprietary or industry-specific protocols for communication with trading venues and counterparties. These protocols may include APIs (Application Programming Interfaces) or message formats specific to the software vendor or trading platform. However, some OMS/EMS providers also support standardized protocols like FIX for connectivity with external systems.\n",
    "### Market Data Protocols\n",
    "For receiving real-time market data feeds from exchanges and data vendors, automated trading systems often use protocols specific to the data provider or exchange. These protocols may include proprietary binary formats optimized for low-latency data transmission. However, some exchanges also offer market data feeds in standardized formats like FIX or JSON (JavaScript Object Notation).\n",
    "### RESTful APIs (Representational State Transfer)\n",
    "Many trading platforms and brokers provide RESTful APIs for programmatic access to their trading services. RESTful APIs use standard HTTP methods (GET, POST, PUT, DELETE) and JSON or XML data formats for communication. They are commonly used for order placement, account management, and accessing historical market data.\n",
    "### WebSocket\n",
    "WebSocket is a protocol that enables full-duplex communication between a client and a server over a single, long-lived connection. It is often used for real-time data streaming in automated trading systems, allowing clients to receive updates from trading platforms or data providers without the overhead of frequent HTTP polling.\n",
    "### Message Queuing Protocols\n",
    "Message queuing protocols like AMQP (Advanced Message Queuing Protocol) or MQTT (Message Queuing Telemetry Transport) are used for asynchronous communication between distributed components of automated trading systems. They provide reliable message delivery, message queuing, and publish-subscribe patterns, making them suitable for building scalable and fault-tolerant trading architectures.\n",
    "### SOAP (Simple Object Access Protocol)\n",
    "Although less common in modern automated trading systems, SOAP is a protocol for exchanging structured information in the implementation of web services. Some legacy trading systems may still use SOAP-based APIs for integration with external systems.\n",
    "\n",
    "These protocols facilitate interoperability between different components of automated trading systems, enabling seamless communication and data exchange across various trading venues, brokers, data providers, and other counterparties. The choice of protocol depends on factors such as performance requirements, compatibility with existing systems, and the preferences of trading firms and technology providers."
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    "# Types of latencies in automated Trading\n",
    "In automated trading, latency refers to the delay between the initiation of a trading action and its execution. Different types of latencies can affect the performance and competitiveness of automated trading strategies. Here are some common types of latencies in automated trading:\n",
    "\n",
    "### Market Data Latency\n",
    "This refers to the delay in receiving market data updates from exchanges or data providers. Market data latency can occur due to factors such as network congestion, processing delays at the exchange, or limitations in data distribution systems. Minimizing market data latency is crucial for capturing timely market information and making informed trading decisions.\n",
    "### Order Latency\n",
    "Order latency refers to the delay between sending a trade order and its execution at the exchange. Order latency can be influenced by factors such as network latency, order processing time at the broker or exchange, and order queue position. Minimizing order latency is essential for executing trades at desired prices and reducing the risk of adverse price movements.\n",
    "### Execution Latency \n",
    "Execution latency refers to the delay between the execution of a trade order and the confirmation of its execution. Execution latency can be affected by factors such as order routing algorithms, market liquidity, and order book dynamics. Minimizing execution latency is critical for achieving optimal trade execution and reducing slippage.\n",
    "### System Latency\n",
    "System latency refers to the delay within the automated trading system itself, including processing delays in strategy execution, risk management, and order management components. System latency can be influenced by factors such as hardware performance, software optimization, and system architecture. Minimizing system latency is essential for ensuring fast and responsive trading system behavior.\n",
    "### Network Latency\n",
    "Network latency refers to the delay in transmitting data between different components of the automated trading system, including servers, data feeds, and trading venues. Network latency can be influenced by factors such as physical distance, network congestion, and the quality of network connections. Minimizing network latency is crucial for maintaining efficient communication and data exchange within the trading infrastructure.\n",
    "### Colocation Latency\n",
    "Colocation latency refers to the delay introduced by the physical distance between the trading system's servers and the exchange's matching engine. Trading firms often colocate their servers in proximity to exchange data centers to minimize colocation latency and gain a speed advantage in executing trades. Colocation latency can significantly impact the competitiveness of high-frequency trading strategies.\n",
    "### Event Latency\n",
    "Event latency refers to the delay in processing market events or trading signals within the automated trading system. Event latency can occur due to factors such as data processing time, event queuing, and algorithmic processing delays. Minimizing event latency is essential for reacting quickly to market changes and executing trades in a timely manner.\n",
    "\n",
    "Each type of latency can have a significant impact on the performance, profitability, and risk exposure of automated trading strategies. Trading firms employ various techniques and technologies, such as high-speed networking, hardware acceleration, and algorithmic optimization, to minimize latency and gain a competitive edge in fast-paced financial markets."
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    "# Types of latency architectures in automated trading \n",
    "\n",
    "In automated trading, latency refers to the delay between the initiation of a trading action and its execution. Different latency architectures are designed to minimize this delay, which is crucial for maintaining competitiveness in high-frequency trading (HFT) and other latency-sensitive trading strategies. Here are some common types of latency architectures:\n",
    "\n",
    "\n",
    "![alt text](https://d1rwhvwstyk9gu.cloudfront.net/2023/01/Cost-and-complexity.jpeg)\n",
    "\n",
    "### Ultra-Low Latency Architecture\n",
    "This architecture aims to achieve the lowest possible latency by optimizing every aspect of the trading infrastructure. It involves using specialized hardware, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), to accelerate critical trading functions. It also includes minimizing network latency by colocating servers in proximity to exchange matching engines, using high-speed direct market access (DMA) connections, and reducing processing time through streamlined algorithms.\n",
    "### Microservices Architecture \n",
    "In this architecture, trading systems are divided into small, independently deployable services called microservices. Each microservice is responsible for a specific trading function, such as market data processing, order management, or risk management. By decoupling components and deploying them on distributed computing infrastructure, microservices architecture can reduce latency by parallelizing tasks and scaling resources dynamically to meet fluctuating demand.\n",
    "### Event-Driven Architecture\n",
    "Event-driven architecture (EDA) is designed to respond quickly to market events and trading signals by processing events asynchronously in real-time. It employs message queuing systems, such as Apache Kafka or RabbitMQ, to decouple components and facilitate high-throughput, low-latency communication. By leveraging event-driven processing, trading systems can react to market changes rapidly and execute trades with minimal delay.\n",
    "### In-Memory Computing Architecture\n",
    "In-memory computing (IMC) architecture involves storing and processing trading data entirely in memory, rather than accessing data from disk storage. By eliminating disk I/O latency, IMC architecture enables faster data retrieval and analysis, leading to reduced latency in decision-making and order execution. In-memory databases, caching systems, and distributed computing frameworks like Apache Ignite or Redis are commonly used in IMC architectures for high-speed data processing.\n",
    "### Cloud-Native Architecture\n",
    "Cloud-native architecture leverages cloud computing infrastructure to build scalable, resilient, and low-latency trading systems. By deploying trading applications in cloud environments, firms can benefit from elastic resource provisioning, geographic distribution, and managed services for data storage, processing, and networking. Cloud-native architectures enable rapid deployment of trading strategies, efficient use of computing resources, and resilience to infrastructure failures, all of which contribute to minimizing latency.\n",
    "### Hybrid Architecture\n",
    "Hybrid architectures combine on-premises and cloud-based infrastructure to leverage the strengths of both environments. For example, latency-sensitive components such as order execution engines may run on dedicated hardware colocated in proximity to exchange matching engines, while other non-latency-sensitive components such as risk management or backtesting systems may be deployed in the cloud for scalability and flexibility. Hybrid architectures offer a balance between ultra-low latency and cost-effectiveness, catering to the diverse \n",
    "needs of trading firms.\n",
    "\n",
    "These latency architectures are continuously evolving as trading firms seek to gain a competitive edge in fast-paced financial markets. The choice of architecture depends on factors such as trading strategy, market conditions, regulatory requirements, and the firm's technological capabilities and resources."
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    "# Levels of sophistication in automated trading \n",
    "Automated trading strategies can vary widely in terms of sophistication, ranging from relatively simple rule-based approaches to highly complex algorithms leveraging advanced machine learning techniques. Here are the levels of sophistication commonly observed in automated trading:\n",
    "### Basic Rules-Based Strategies \n",
    "These are the simplest form of automated trading strategies, typically relying on predefined rules or conditions to make trading decisions. Examples include moving average crossovers, support and resistance levels, or simple trend-following rules. These strategies are easy to implement and understand but may lack adaptability to changing market conditions.\n",
    "### Technical Indicator-Based Strategies\n",
    "These strategies incorporate technical indicators, such as Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), or Bollinger Bands, to generate trading signals. Traders may combine multiple indicators or use them in conjunction with price action patterns to increase the robustness of the strategy. While more sophisticated than basic rules-based strategies, these approaches still rely on static rules and may struggle in volatile or choppy markets.\n",
    "### Quantitative Strategies\n",
    "Quantitative trading strategies involve the systematic analysis of historical data using statistical and mathematical models to identify trading opportunities. These strategies may include mean-reversion strategies, pairs trading, or statistical arbitrage. Quantitative traders often conduct extensive backtesting and optimization to validate their models and assess performance under different market conditions.\n",
    "### Machine Learning-Based Strategies\n",
    "Machine learning techniques, such as supervised learning, unsupervised learning, and reinforcement learning, are increasingly being applied to automated trading. These strategies learn from historical data to identify patterns, correlations, and anomalies that traditional models may overlook. Machine learning algorithms can adapt to changing market dynamics and extract insights from large datasets, potentially leading to more adaptive and predictive trading strategies.\n",
    "### High-Frequency Trading (HFT) Strategies\n",
    "HFT strategies operate on extremely short timeframes, typically executing trades in microseconds or milliseconds. These strategies exploit market inefficiencies, liquidity imbalances, and order flow dynamics to profit from small price movements. HFT strategies require ultra-low latency infrastructure, high-speed data feeds, and advanced order execution algorithms to maintain competitiveness in fast-paced markets.\n",
    "### Algorithmic Market Making Strategies\n",
    "Market making strategies involve continuously providing liquidity to the market by quoting bid and ask prices for financial instruments. Algorithmic market makers adjust their quotes dynamically based on factors such as order flow, market volatility, and inventory risk. These strategies require sophisticated pricing models, risk management techniques, and optimal order execution algorithms to manage inventory and minimize adverse selection.\n",
    "### Neural Network-Based Strategies\n",
    "Neural networks, a subset of machine learning, offer the capability to model complex, nonlinear relationships in financial data. Neural network-based trading strategies use deep learning architectures to extract features from raw market data and make predictions about future price movements. These strategies can capture intricate patterns and dependencies in data but require significant computational resources and careful tuning to avoid overfitting.\n",
    "\n",
    "\n",
    "The level of sophistication in automated trading strategies depends on various factors, including the trader's expertise, available resources, market conditions, and regulatory constraints. While more sophisticated strategies may offer the potential for higher returns, they also come with increased complexity, computational requirements, and risks of model failure or overfitting. Traders should carefully assess the trade-offs and suitability of different strategies based on their objectives and constraints."
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    "# Advantages of using automated trading systems\n",
    "The advantages of using automated trading systems are as follows:\n",
    "\n",
    "**Ease of use**: Orders can be placed quickly with the help of the software navigating you through the different parameters. Moreover, your own automated system will make it even easier for you to place orders since it is always at your disposal.\n",
    "\n",
    "**Live portfolio review and market updates**: With the automated trading system, you can view market data of financial instruments such as shares, commodities, stock indices, etc anytime. You can also keep track of your portfolio, its underlying assets and performance with the software.\n",
    "\n",
    "**Notification feature**: One of the most advantageous features of the automated trading system is the notification feature. The automated system will send you alerts to notify you about the latest events in your portfolio as well as broker recommendations.\n",
    "\n",
    "**Related news updates**:  Users can also track live updates on developments related to a particular stock or segment with the help of automated systems. The system helps you to be updated so as to modify your trade related inputs such as the selection of financial instruments, updating of the stop loss value, limit value etc.\n",
    "\n",
    "**Analysis and charts**: Another feature is the provision of historical charts and analysis that allows you to look at previous indices data, stock prices and provides analytical tools for the same."
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    "# Disadvantages of using automated trading systems\n",
    "couple of disadvantages of using the automated trading systems, which are:\n",
    "\n",
    "**Expenses to use the automated system**: The automated trading system comes with an expense and that may be a disadvantage for some who do not wish to invest in the same. Also, building the automated system will take some expenses such as hiring a developer to help with coding part while building the system, etc.\n",
    "\n",
    "**Connectivity**: A generic disadvantage of using the automated trading system is connectivity. In some remote regions, connectivity disturbances are common, and this may lead to losses during order placements.\n"
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