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We detail a system architecture for a high-performance automated trading platform used by hedge funds in equities markets. The platform is optimized for ultra-low latency (nanosecond to microsecond scale) high-frequency trading. It enables quantitative developers to deploy custom algorithmic strategies that react to real-time market data and execute trades automatically, all while enforcing strict risk controls. Notably, the design scope is limited to the real-time trading engine – features like portfolio management, GUI dashboards, or account reporting are intentionally omitted to focus on speed and trading logic.
The platform exposes a Strategy API to the quantitative developers, allowing them to develop custom trading algorithms that plug into the system. This API is not a web API but a code-level interface (library/SDK) given the need for ultra-low latency. Developers write their strategy logic following a prescribed format, and the platform loads and executes this logic in real time.
Key aspects of the Strategy API include:
onTick() or onOrderBookUpdate() that the platform will call for each new market event. These callbacks pass essential data (e.g. best bid/ask price, full order book, last trade, etc.) in a struct or object format that’s optimized for speed (possibly using pre-allocated memory to avoid garbage collection).sendOrder(instrument, price, size, side, type) for placing an order (market or limit) or cancelOrder(orderId) for cancellations. When a strategy calls these, the platform’s underlying engine intercepts the request, runs it through risk checks, and then forwards it to the exchange if approved. From the strategy developer’s perspective, these are synchronous calls that either succeed (order accepted by system) or throw an exception/return an error if blocked by risk or system issues.onOrderAck(orderId) might notify that an order was accepted by the exchange, and onTrade(orderId, price, volume) notifies a fill (execution). This keeps strategy logic informed of its orders’ statuses in real time, which is critical for strategies to update their internal state (e.g. current position) accordingly.onStart() (called when the strategy begins) and onStop() (on shutdown) to initialize or cleanup resources. They can also fetch configuration parameters (like their risk limits, trading symbol lists, etc.) through the API at startup. The platform may load strategy code as a plugin (for example, a .so library if written in C++). Each strategy runs isolated, but the API ensures they can’t accidentally interfere with each other’s data.Because performance is critical, the API is likely implemented in a low-level language. Commonly, these platforms use C++ for strategy code to maximize speed and control over memory. In some cases, a higher-level language wrapper might be provided for convenience (for instance, a Python interface that under the hood compiles to C++ or executes in a less latency-critical environment), but for nanosecond-level trading, strategies generally need to be native code. The API design therefore balances ease of use with the need for the strategy to run essentially as optimized as if it were part of the engine itself. It provides just the essentials for real-time trading logic – there are no calls for GUI updates or long-running blocking I/O. Even logging from within the strategy is either disallowed or provided via a non-blocking API (e.g., logging to a memory buffer) to avoid slowing down the critical path.
Additionally, an Administration API (for internal use) might be provided to control the system: e.g., to deploy new strategy code, start or stop strategies, adjust risk limits, or query system status. This could be a command-line interface or a lightweight service since it’s not time-critical. For example, an admin could call a function to load a new strategy module into the engine and activate it on a given data feed. The admin API ensures that strategies can be updated between trading sessions or even intraday with minimal downtime.
For the Backtesting environment, the API is designed to be as identical as possible to the live trading API. A strategy that implements onTick() and sendOrder() should work unchanged in backtest mode. Under the hood, in backtest, sendOrder doesn’t actually go to an exchange but to a simulator that will fill the order based on historical data. This consistency allows developers to write a strategy once and test it thoroughly on historical data using the same interfaces, increasing confidence that it will behave the same way in production.
At a high level, the trading platform is composed of several interacting subsystems that handle data input, decision processing (strategies), risk checks, and output to markets. Each co-location deployment will run an instance of these components to trade on the local exchange(s). A conceptual architecture diagram is shown below, illustrating how market data flows in, strategies operate, and orders flow out:
Overview of the high-frequency trading platform’s core components and data flow. Market data from exchanges is captured, fed into strategy engines (algorithmic trading models), passed through risk management (RMS), and then sent as orders to exchanges via an Order Execution system (OMS/EMS). The architecture emphasizes low-latency paths and co-located deployment.
Market Data Ingestion
The platform includes a Market Data Feed Handler (or Feed Ingest module) that connects directly to exchange data feeds. In a co-location facility, this means using the exchange’s local multicast or point-to-point feeds to get order book updates and trades with minimal network latency. The feed handler is responsible for receiving raw market data packets (using protocols like Nasdaq ITCH or standard FIX messages) and decoding them into a structured form. It updates an internal representation of the market state (e.g., an order book for each instrument) and then disseminates these updates to the strategy logic. The dissemination can be done via a high-speed messaging bus or by direct function calls to strategies, depending on the integration mode (discussed later). The feed handler is highly optimized – using kernel-bypass network drivers and FPGA-based network cards if necessary – to handle the high event rate without dropping updates. It may also time-stamp each event upon arrival for latency measurements. This component operates continuously during market hours, pumping data into the rest of the system.
Strategy Engine
At the core of the system is the Strategy Execution Engine, which hosts user-developed strategies (the algorithms). Think of this as a container or scheduler for strategy instances. Each strategy instance subscribes to certain data (for example, one strategy might only trade a specific set of stocks). When market data updates arrive, the engine invokes the corresponding strategy’s callback with the new data. The strategy code executes (very quickly, ideally just a few microseconds of logic) and may decide to send one or more orders via the API. Strategies run concurrently, each essentially consuming the same market feed (if they trade overlapping instruments) or separate parts of it. The engine ensures isolation between strategies – one strategy’s processing should not stall others. This can be achieved by dedicating separate CPU cores or threads to different strategies or groups of strategies. The Strategy Engine is carefully designed to add near-zero overhead beyond the strategy code itself; it’s essentially glue connecting data to strategy and strategy to orders. If the platform supports multiple programming languages or a scripting layer, the engine also manages those (for example, a JIT compiler if strategies are in a scripted language). However, typically for HFT, strategies are native code.
Risk Management
Before any order goes out, it flows through the Risk Management Module. At a high level, the Risk module monitors the overall trading activity and exposures in real time. It enforces predefined constraints: e.g., no strategy can exceed a position of N shares in any stock, or send more than M orders per second, or lose more than $X without halting. The risk system receives updates on trades and positions (either by snooping on strategy outputs or via the Order Execution module feedback) and keeps an internal ledger of each strategy’s current risk metrics. When a new order is submitted by a strategy, the risk module intercepts it (synchronously) and checks it against limits. If the order would violate a limit (say, it’s trying to buy more shares than allowed), the risk module rejects or downsizes it, and the strategy is informed (likely via an exception or a callback). If within limits, the order is approved to go out. The risk module operates with microsecond latency as well – its checks are simple arithmetic comparisons or lookups in memory, so they should add only maybe a microsecond to the process. In addition, the risk system can have a kill-switch: if it detects something seriously wrong (e.g., a strategy has lost too much or is behaving erratically), it can block all further orders from that strategy or even cancel its open orders by signaling the Order Execution component. The risk module may also incorporate regulatory checks (like compliance with exchange-level rules on order rate or certain safety net like “stop trading if volatility is too high”), ensuring the firm doesn’t violate exchange policies.
Order Execution (Trading Gateway)
This subsystem is responsible for interfacing with the exchange’s trading system to actually place and manage orders. It typically maintains one or more sessions (connections) to each exchange’s order entry gateway. In equities, this could be via FIX protocol or more often a proprietary binary protocol for lower latency (e.g., OUCH for Nasdaq orders). The Order Execution component receives orders that have passed risk checks and formats them into the appropriate outgoing message. It might maintain an internal Order Management System (OMS) state – essentially tracking all active orders, their exchange IDs, etc., so that when fills or acknowledgments come back, it can match them to the originating strategy order. The execution gateway is also tuned for latency: it uses non-blocking I/O, possibly sends messages directly from user-space via kernel bypass (e.g., using Solarflare OpenOnload or DPDK for networking), to avoid context switches. The module immediately relays any responses: when the exchange confirms an order or sends a trade execution report, the gateway captures that and feeds it back to the Strategy Engine (and Risk) so the strategy knows the order status. This feedback loop is crucial to keep the strategy’s view of the world consistent (e.g., if an order is partially filled, the strategy’s position changes and risk usage changes). The Order Execution module often also handles cancellations and mass cancel in emergencies (for example, on a stop signal it can cancel all outstanding orders).
Backtesting Framework
Separate from the live trading loop, the architecture includes a backtesting subsystem. This can be thought of as a parallel environment that reuses the Strategy Engine and possibly the Risk module, but instead of live market data, it plays back historical data from the database, and instead of sending orders to a real exchange, it sends them to a simulator. A user can run a backtest by specifying a time range and instrument universe; the system will load the historical ticks for that period (from the historical data store) and feed them into the strategy at the same pace (or faster, if doing accelerated simulation). The strategy will issue orders, and a Simulated Exchange component will determine trade outcomes. This might involve simulating an order book – for example, if the strategy’s order would have been the best bid, the simulator can fill it if the historical data shows a trade happened at that price, etc. A simpler approach is to assume the strategy’s marketable orders get filled at historical prices with maybe some slippage. The backtest framework keeps track of P&L and risk like the live system, but all in a sandbox. It likely produces a report at the end (e.g., profits, maximum drawdown, etc.). Backtesting is not real-time critical, so it can afford to use disk I/O and slower processing, but it should still aim to simulate the strategy reasonably fast (potentially multi-threading across days or instruments for speed). The key design principle is that the same strategy code runs in backtest and live – ensuring fidelity of results.
Infrastructure and Deployment
The system is deployed on high-performance servers in co-location facilities. Typically, each exchange or region has its own instance of the platform physically located near that exchange to minimize network latency. For example, one instance might run in Secaucus, NJ for Nasdaq, another in Mahwah, NJ for NYSE, etc. Each instance can run multiple strategies that trade the local markets. A global control might oversee these instances (for example, a central risk dashboard might aggregate exposures across all instances, but actual trading is local). Within each instance, the components (feed handler, strategies, risk, execution) could all reside on a single physical machine for pure minimal latency – indeed, colocating all components in one server and using in-memory communication can reduce latency to sub-microsecond levels. Alternatively, for load distribution, the feed handling could be on one server and strategy on another, but that introduces an interconnect latency (even with 10GbE, that might add a few microseconds). Many HFT platforms choose to keep the critical path within one process or machine. Our design leans toward consolidating components on one server per trading unit, with careful multi-threading. However, to achieve fault tolerance, certain components might be replicated: e.g. a secondary feed handler process can run in parallel as a hot standby, or two strategy processes could run the same strategy (one active, one shadowing ready to take over). The system also includes redundant network links and power supplies to reduce the chance of an outage. If one entire co-lo site goes down (power failure, etc.), the worst-case scenario is that the strategies in that site stop trading – which is mitigated by the fact that other sites (trading other markets) are unaffected. For the same market, true active-active across data centers is hard due to latency differences, but the system could have a disaster recovery site that can come online (with higher latency) if the primary fails, mainly to flatten positions or manage risk until the primary is restored.
Overall, the high-level design emphasizes a pipeline: Market Data -> Strategy -> Risk -> Order Execution -> Exchange, with backtesting as a parallel loop using recorded data. Each component is optimized and integrated to minimize copies and delays, achieving the required speed and supporting the heavy load of HFT operations.
In this section, we delve into each major component of the system, explaining their internal design, technologies, and how they meet the performance requirements:
The Market Data Feed Handler is the gateway for all incoming market information. Its design must handle extremely high input rates with negligible latency. It typically runs as a dedicated thread or process pinned to a CPU core, so it can busy-wait on the network socket without interruptions (polling the NIC for new packets continuously). We employ kernel-bypass networking (such as Solarflare OpenOnload or DPDK) so that incoming packets are delivered directly to user-space memory, avoiding context switches. The NIC hardware might also be configured for RSS (Receive Side Scaling) to distribute different data streams (e.g., different symbols or feed channels) to different CPU cores, if scaling is needed.
Once a packet arrives, the feed handler parses it using optimized code. For example, Nasdaq’s ITCH feed is a binary protocol; we would implement a highly efficient parser in C/C++ that can decode messages (which are often just a few bytes long) in a few nanoseconds each. We minimize memory allocations by reusing buffers. If maintaining a full limit order book, the feed handler updates the order book data structure. These order books are kept in-memory using arrays or trees keyed by price. A lot of attention is paid to data structures here: since updates are frequent, we use structures that offer O(1) or O(log n) update times and are cache-friendly. For instance, one might use an array indexed by price level for depth (common in futures) or a binary search tree for price orders. Given the enormous throughput, even minor inefficiencies could become bottlenecks.
After processing, the feed handler distributes the update to strategies. There are a few design approaches for this:
The feed handler also might incorporate a small fail-safe buffer: if strategies are momentarily slow, the handler can queue a few messages. But ideally, strategies keep up so the queue stays near empty to avoid latency buildup. In case of extremely bursty input (e.g. big market news causing hundreds of thousands of events in a second), the feed handler must have some back-pressure mechanism – perhaps it can drop less critical updates (like if a price changes 10 times in one millisecond, maybe skipping some intermediate ticks if they’re outdated by the time we process them). However, dropping data is generally last-resort since it could affect strategy decisions; designing for sufficient capacity (as per our capacity estimates) is preferred so dropping isn’t needed.
Technology choices
We use C++ for the feed handler for maximum performance and pointer-level control. If even more performance is needed, some firms move feed handling to FPGA cards – the FPGA can parse the feed and even maintain an order book at hardware speeds (nanoseconds) and then feed updates to the CPU. That approach can reduce feed-to-strategy latency significantly, but it’s complex to implement. In our design, we assume a pure software feed handler but keep the interface open for future FPGA acceleration (perhaps sending FPGA-parsed data into the same strategy API format). The feed handler is also set up with redundancy: typically two feed handlers might run in parallel, each connected to a different exchange data line (exchanges often provide multiple redundant feeds). One acts as primary and the other as backup (comparing sequence numbers, etc.). If the primary misses a packet or fails, the backup can fill in or take over without the strategies noticing a gap.
The Strategy Engine is essentially the runtime that loads user strategies and executes them on incoming data. Each strategy could be a plugin (a .dll/.so library) implementing a known interface. The engine will dynamically load these at startup or on deployment. Strategies might also run in isolated processes for safety. Let’s consider two modes:
In-Process Threads
All strategies run as separate threads in the same process as the feed handler and order execution. This maximizes speed because a market data update can be given to the strategy via a simple function call or pointer passing. We can assign each strategy thread a core (affinity) and perhaps use CPU isolation (so the OS scheduler doesn’t context-switch them unnecessarily). Communication between feed handler thread and strategy threads can be via the lock-free ring buffer or shared memory as described. The risk checks and order sending can be done with direct calls as well. The advantage is minimal inter-process communication overhead; the disadvantage is that a crash in a strategy (segfault, etc.) could potentially affect the whole process (though that can be mitigated by good isolation and error handling).
Multi-Process (Microservice) Model
Each strategy runs in its own process (or at least separate from the main engine). They could even be on separate machines, though that’s not typical for HFT due to latency. In a multi-process same-machine scenario, the feed handler would publish data to all strategy processes via shared memory or a very fast network loopback. The strategies then send orders back to a central order manager process via IPC. This adds perhaps a few microseconds latency but improves fault isolation (one strategy crash doesn’t crash others). It also makes scaling easier – you could run strategies on different physical servers if you ever needed to distribute load (but then you incur network latency between feed and strategy servers). For our design, we lean toward in-process for maximal performance, but with careful coding to handle failures (e.g. wrapping strategy calls in try/catch, and having the risk module or a supervisor able to disable a misbehaving strategy thread).
Inside the strategy engine, when a strategy’s callback (like onTick) is invoked, it is expected to run to completion quickly. We impose a guideline that strategy code should not perform any blocking operations (no sleep, no waiting on locks that could stall if something else is slow). Essentially, strategies should compute a few arithmetic operations or consult a small in-memory array and decide an action. If a strategy needs to do something heavier (like compute a large matrix or call an external service), it should ideally do that outside the real-time callback (for example, pre-compute certain things or run a slow analysis on a separate thread that doesn’t block trading). The strategy engine can provide a utility for scheduling such background tasks if needed, but that’s beyond core trading loop.
The Strategy Engine also handles timers or scheduled events. For example, if a strategy wants a function called every second (maybe to clear some stats or to send a heartbeat), the engine can provide a high-resolution timer that triggers a callback. This should be implemented with a precise timestamp so it doesn’t drift (we can use CPU TSC or hardware timers for sub-millisecond accuracy). These scheduled events again run on the strategy’s core.
Memory management within the strategy engine is important: to avoid unpredictable pauses, we might have the engine pre-allocate memory pools for each strategy (for things like order objects, etc.) so that during trading, no dynamic memory allocation is needed (which could trigger a slow malloc or GC). Similarly, if using a language like Java or .NET (less common in HFT but possible), one would use off-heap memory or preallocated direct byte buffers to avoid garbage collection delays. Given our focus, we likely stick to C++ with no GC, but even C++ new/delete could fragment memory – hence object pools and reuse are the norm.
The Risk Management subsystem runs concurrently with strategies, but it lies in the path of order execution. It can be conceptualized as a set of checks that execute almost instantaneously whenever an order is placed. To achieve this, the risk module keeps all needed data in memory and uses simple operations. Here’s how it’s structured internally:
The challenge is if risk checks become numerous or complex. We mitigate this by focusing on the most essential and quantifiable checks in the automated path. We do not, for instance, do heavy computations like value-at-risk or stress tests in real time here – those would be too slow. Those are done offline or at a slower cadence. The real-time risk is more about hard limits and simple calculations.
To avoid the risk module itself becoming a bottleneck, it can be integrated into the order execution thread. For example, the same thread that sends orders could perform the risk check just before sending. This way, we avoid context switching to a separate risk thread. Another design is to have a dedicated risk thread that listens for order requests, processes them, and forwards to execution. That adds queueing and context switch overhead, so likely we prefer the integrated approach. However, a separate thread could be useful if we want to parallelize – e.g., the strategy thread hands off the order to risk thread so the strategy can continue processing next tick without waiting. But since strategies typically don’t produce orders at a rate that would overwhelm a single thread’s ability to check (50k orders/sec as assumed, one core can handle that many comparisons easily), a synchronous inline check is fine.
The Risk module also monitors aggregate conditions. For example, overall firm-wide exposure. If multiple strategies collectively are only allowed to use certain capital, the risk module might subscribe to all their activity and if an aggregate limit is hit, it could intervene. This introduces a slight complexity – if strategies are independent threads, a global risk monitor thread might asynchronously signal them or the order system to stop. This could be done via a flag that strategies check or simply by the risk system refusing further orders. In practice, each strategy has its own limits, and global limits are enforced by assigning appropriate sub-limits, to avoid needing a cross-strategy mutex in real-time.
Importantly, the Risk module design includes a kill-switch or panic button logic. If it detects something truly anomalous (like a strategy is putting in orders that don’t make sense or positions getting too large), it can issue a command to flat all positions and disable the strategy. This might be manual (triggered by an operator or an external system that oversees risk) or automatic. To implement this, the risk module would on a trigger iterate over all that strategy’s open orders (which it tracks via the OMS state) and send cancels, and mark the strategy as frozen (so any new order from it is rejected). These actions must be done extremely quickly as well – essentially in an emergency, we sacrifice a bit of latency to send a flurry of cancellation messages, but that’s acceptable since it’s better to stop losses. Exchanges also often provide a “cancel on disconnect” feature; as a backup, if we, for example, intentionally drop the connection, the exchange will cancel all our orders. The system might do that as a last resort if it cannot individually cancel fast enough.
The Order Execution Gateway (trading interface) is designed to be the fastest possible path to the exchange. It often has to interface with exchange APIs that might not be trivial – some exchanges require login, sequence numbers, heartbeats, etc. Our design includes a session manager that handles the protocol details: logging in at start of day, sending heartbeat messages to keep the session alive, handling sequence resets or recovery if needed (in case of a missed acknowledgment, etc.).
When an order comes through (post-risk), the gateway module assigns it an internal ID and maps it to an outgoing exchange message. Many HFT systems will maintain a pool of pre-constructed message byte buffers for orders – e.g., a template of a limit order message where you just fill in price and quantity – so you don’t spend time formatting strings or doing heap allocation. We’ll do similar: have a memory region where we build the message (which might be a fixed-length binary message). Then we call the low-level network send. Using kernel bypass or an asynchronous send, we can often push the packet out without entering the kernel. This results in very low latency.
The network connectivity to exchanges might be over fiber within the same data center, so latency is on the order of microseconds. We also ensure our server’s NIC is tuned: features like interrupt moderation are turned off (we prefer to poll rather than wait for an interrupt), and the NIC may be in an exclusive mode for our process.
For handling acknowledgments and fills: when the exchange matching engine processes our order, it will send back different messages: an acknowledgment (order accepted and live) or an immediate fill (if it crossed with existing orders) or later fills, or cancel confirmations, etc. These come in via presumably the same session or a separate session (some exchanges have a separate feed for executions called a “drop copy”). The gateway must parse these quickly (again using pre-written parsing code) and then update the internal order state. The internal state likely is a map from our internal order ID to its status (open with X remaining, or filled, or canceled). That state is important for the strategy to know what’s going on. After updating, the gateway calls into the Strategy Engine to dispatch the relevant callback (or if using a message queue internally, it enqueues an event “order X filled 100 shares”). The strategy then handles it. Meanwhile, the risk module could also be notified from here or via the strategy itself updating position.
If an acknowledgment doesn’t come after a short time, the gateway might resend or at least mark something as problematic (exchanges and our system have sequence numbers to detect drops). We want to avoid hanging orders.
The gateway design also includes rate limiting and queuing logic. Exchanges often specify a max message rate or risk penalties if you exceed certain thresholds. If our strategies collectively try to send 100k orders/sec but the exchange only allows 50k, we need to throttle. The gateway can monitor the outgoing rate and either queue some orders or signal backpressure to strategies. A simple approach is a leaky bucket algorithm: allow orders at the max rate and enqueue excess to send slightly later. This does introduce a tiny delay for those throttled orders, but that’s necessary to comply with exchange rules (and better than the exchange rejecting them or disconnecting us). In practice, hitting the throttle limit is rare if strategies are calibrated, but the system should guard against runaway algorithms.
Finally, the gateway has robust error handling – e.g., if the exchange sends a reject (maybe an order was malformed or a stock is halted), we log that and pass it to the strategy (maybe via an onError callback). If the network disconnects, the gateway immediately informs risk/strategies and perhaps triggers the cancel-on-disconnect as mentioned.
The Backtesting Module is structured as an offline replica of the trading engine, with a few key differences: it reads from a database instead of a live feed, and it uses a simulated exchange rather than a real one. The module likely runs as a separate application or a mode of the main application (perhaps launched with a flag indicating a backtest run). One could even run backtests on separate hardware (like not in co-lo, but on a compute cluster or cloud, since latency is less critical there).
Historical Data Player
This sub-component handles retrieving historical data and feeding it to strategies. It might load data for one or multiple symbols. Efficiency is crucial because tick data is huge – we might memory-map files for the tick data to allow sequential reading at C speed, or use the time-series DB’s API to stream it. The player then sorts or merges multiple feed streams if needed (for multi-symbol strategies, we need to merge streams by timestamp order to present to the strategy exactly as things happened). Then, it sends these events into the Strategy Engine. It can reuse the same feed handler logic but pointed at a file instead of a socket. In fact, code reuse is desirable: we can abstract the feed handler so it can operate from recorded data as well. This ensures the format and sequence of events is identical to live (including any oddities like out-of-order ticks or corrections, if they exist in live, the backtester should replicate them).
Simulator (Exchange & Market Impact)
When strategies place orders in backtest, the simulator must determine outcomes. The simplest simulator assumes no market impact from our orders – i.e., the market data we replay is unaffected by the strategy. This is okay if the strategy is small enough not to move the market. The simulator can then fill orders if the historical data shows price reaching the order’s level. For example, if a strategy placed a limit buy at 148, clearly our buy would have filled at 149 ask was hit). We might simulate that it filled when the first trade <= 149 and 5000 shares traded at $149 in the next second, if our order was 500 shares we assume it was among those 5000 and got filled entirely).
We also incorporate simulated latency if needed: e.g., in live trading there’s a small delay from when we decide to when the exchange receives the order. In backtest, everything can appear instantaneous unless we simulate it. If the strategy is sensitive to latency (say an arbitrage that might fail if you’re 100 microseconds behind), we might simulate that by slightly delaying our order placement relative to the historical timeline. This is an advanced detail, but it can be important for high-frequency strategies.
Parallel Backtesting
The design should allow running multiple backtests concurrently on different threads or machines. For example, testing different parameter variations or different days in parallel, as historical simulation is not real-time dependent. We can spin up multiple instances of the backtest module, each isolated. This doesn’t affect the live system but is a feature of the platform to speed up research.
Results and Analysis
After a backtest run, the module generates output: maybe a list of all trades the strategy would have made, P&L over time, and risk metrics. This can be saved to a file or database for the researcher to examine. The platform might integrate with analysis tools (like Python libraries for plotting equity curves, etc.), though those are outside the core engine.
The important design aspect is that the strategy code is reused – it’s literally the same binary or code path as in live trading, which helps ensure that if it worked in backtest, it behaves the same live (barring market impact differences). The backtest module is essentially a time-travel sandbox for strategies.
Although not directly asked for UI or dashboards, the system will include some monitoring hooks. For example, each component can expose metrics (latency stats, message rates, queue lengths, CPU usage) to a log or a telemetry system. We might run a lightweight stats collector thread that aggregates how many ticks processed per second, what the average tick-to-order time is, etc. These can be written to a console or network to an external monitor system. This is important in practice to ensure the system is healthy (e.g., if latency suddenly spikes, we want to catch it).
Fault tolerance is achieved through redundancy in critical components:
Each component is designed with the worst-case scenario in mind: For example, if the feed handler gets overloaded or fails, ensure it doesn’t bring down the strategy engine (in-process, we protect with try/catch around parsing; out-of-process, the engine notices if heartbeats from feed stop and can alert). If the order gateway loses connection, the risk module should freeze trading until reconnected. These interactions have been described in Request Flows above as well.
In the live trading path, the use of traditional databases is minimized in order to meet latency requirements. Most data needed for real-time decision making (market prices, positions, etc.) is kept in-memory. However, a few specialized data stores are part of the overall system architecture:
Configuration and Reference Data Store
There is a lightweight database or configuration repository that holds static or semi-static data: for example, exchange connection details, instrument metadata (tick size, lot size, etc.), user strategy configurations (which symbols each strategy trades, risk limit values, etc.), and perhaps credentials or keys for exchange APIs. This could be a simple relational DB or even a file-based store loaded at startup, since reads/writes here are infrequent (mostly at startup or strategy deployment times). The size is small (perhaps a few MB at most of config data). It’s acceptable for this store to have millisecond latencies because it’s not in the trading loop.
Market Data Historical Database
For backtesting and research, the platform needs to maintain a large repository of historical tick data (order books, trades, maybe even full depth order book snapshots). This is a big-data component. A suitable design might use a columnar time-series database or file system optimized for sequential read speed – for example, Kdb+ (a popular time-series DB in HFT) or a modern distributed file store with Parquet files. The key is that it can retrieve months of tick-by-tick data quickly and feed it to the backtest engine. Data is likely partitioned by date and instrument. We may store compressed tick data on disk and load chunks into memory for playback. The storage system should support throughput on the order of hundreds of thousands of ticks per second for each backtest stream to realistically simulate real-time (and possibly faster-than-real-time for batch backtests). If needed, the historical data could be stored in a cluster separate from the trading engine (for example, in a high-capacity cloud storage or a dedicated NAS), since it’s not needed for live trading operations.
Real-Time Data Cache/Store
In some designs, we might keep a short rolling window of recent market data or trading activity in an in-memory cache, to facilitate quick access or for use by risk management. For instance, the risk module might want to know the last traded price of each stock or the volume in the last minute. Rather than querying an external DB, that information is maintained in memory by the feed handler or risk engine. We avoid any synchronous calls to a database in the trading path; everything is either in memory or passed along with events.
Order and Trade Logs
Every order sent and trade executed should be recorded for audit and analysis. Instead of a traditional DB table insert per trade (which would be too slow at high rates), the platform can simply append to log files or use a high-performance logging system. For example, it could write out each execution event to a local file (or in-memory buffer to be flushed) with a timestamp. These logs can later be loaded into a database offline if needed (for compliance or P&L analysis). Some firms use a message bus to publish executions which are then picked up by a separate process and inserted into a database asynchronously. In our design, since we omit portfolio reporting, we assume a simple log-to-disk approach with minimal overhead on the main engine.
Our database design segregates the concerns: configs and limits in a small store (for reliability and ease of updates), heavy historical market data in a specialized high-throughput store for backtesting, and logging of real-time events to files or an async database for record-keeping. All critical live data needed for decisions is kept in memory or on the wire (e.g., the latest market state is in-memory, not constantly fetched from any DB). This design is aligned with low-latency best practices and avoid unnecessary I/O and keep the data close to the processing.