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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Build a paper-trading app as three connected systems: a market-data feed, an order simulator, and a portfolio ledger. Keep their responsibilities separate, make feed and fill assumptions visible, and record each order and fill as an auditable event. A paper trade remains a simulation even when it uses real-time prices; it cannot reproduce every live-market execution effect.
Plan the app around three separate systems
A clean architecture prevents market-data updates, simulated execution, and portfolio calculations from becoming one opaque process. The data feed supplies observations; the simulator turns eligible orders into simulated fills; the ledger applies those fills to cash and positions.
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- Market-data service: receives vendor data, normalizes it, and preserves its timestamp and feed identity.
- Order simulator: validates submissions, tracks order states, and applies the fill rules you disclose.
- Portfolio ledger: records order and fill events and derives cash, holdings, and history from them.
Alpaca is one documented implementation option, not a universal specification. Its Paper Trading documentation describes a simulated environment, while its Trading API overview lists supported order types. Check current support for the assets and API version you choose.
Choose and identify the market-data feed
Start by deciding which assets and regions the app will support. Then choose a data source whose coverage and account entitlements match that scope. Do not label a feed simply “real-time” without identifying what the user is actually entitled to receive.
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Alpaca’s documentation describes real-time and historical data for equities and crypto. Its paper-only account documentation specifically says those accounts are entitled to IEX market data; that does not mean a paper account automatically receives consolidated market data. See the Alpaca documentation overview and the US documentation index for the vendor’s product and data scope.
Normalize data without losing provenance
Convert vendor messages into an internal format that preserves, at minimum, the symbol, event timestamp, relevant price and size fields, and the source or feed identity. Retaining those fields lets your simulator and interface distinguish observations from different feeds and helps explain why two users might see different data.
Handle missing or stale observations
Decide what the simulator does when a quote or trade is absent, delayed, or stale. For example, it can refuse to evaluate an order until it receives a sufficiently recent observation, while showing the user why evaluation is paused. Make the rule explicit and apply it consistently; silently using an old price can make both fills and portfolio values misleading.
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Define order states and fill rules
An order needs a visible lifecycle, not just a submitted flag and a final price. A useful model can include accepted, pending, eligible, partially filled, filled, canceled, and rejected states. Which states apply depends on your simulator and supported order types.
Specify eligibility and price selection
For each order type, define what market observation makes the order eligible and how the app selects a simulated fill price. For a limit order, state when the market is considered marketable against the user’s limit. Alpaca’s paper-trading documentation describes marketability conditions for limit orders and says eligible orders can receive partial fills; it does not make those rules a complete specification for every custom simulator.
Also decide whether an eligible order fills all at once or can fill partially, and what happens when data is missing or the order is canceled while pending. Those are model choices. Document them in plain language beside the order experience, not only in developer notes.
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Be clear about what a hosted simulation omits
Alpaca says its paper environment matches orders against the best available current market price (NBBO), but its documentation also lists live-market effects it does not account for: market impact, information leakage, latency-related slippage, queue position for non-marketable limit orders, and market-data sources. Consequently, simulated performance is not a reliable promise of live execution or results. Put this limitation near performance figures and simulated fills.
Alpaca states: “However, please note that paper trading is only a simulation. It provides a good approximation for what one might expect in real trading, but it is not a substitute for real trading and performance may differ.” — Alpaca Markets, Paper Trading documentation.
Build the portfolio from recorded fills
Make fills—not display-only price changes—the events that alter simulated cash and positions. Persist enough information to reconstruct each change, such as the related order, fill quantity and price, and event time. Show order history alongside current holdings so users can trace how the displayed portfolio came to be.
This event-ledger approach is an architectural recommendation, not a vendor-mandated data model. It also gives you a practical way to reconcile an account: replay recorded fill events and compare the computed cash and positions with the values shown in the app.
Decide how to treat dividends and corporate actions
Alpaca says its paper account does not simulate dividends. If your app presents total returns or compares performance over time, decide whether dividends and other corporate actions are outside scope or are handled through a separate, explicit policy. Do not imply that a portfolio includes those effects when it does not.
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Test the rules and recovery paths
Test behavior against the simulator’s stated rules, including cases where the order is straightforward and cases where the data or connection is not. Alpaca’s documentation notes live-trading issues such as orders that may not fill, price spikes, and network disconnections; these are useful failure modes to consider when designing and testing a simulator, not evidence that your implementation has been tested.
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- Market orders and limit orders, including crossing and non-crossing limits.
- Partial fills, cancellations, and rejected orders.
- Missing, stale, or interrupted market data.
- Duplicate submissions and reconnect or retry behavior.
- Portfolio recomputation from the recorded order and fill history.
For retry handling, make sure a repeated request cannot accidentally create an unintended second simulated order. For recovery, define how the app restores order state and portfolio values from durable events rather than relying on in-memory state alone.
Choose hosted APIs or a custom simulator deliberately
A hosted paper-trading API can provide an account and order workflow for a prototype; building your own simulator gives you more control over its rules and event history. Compare options against the needs of your product rather than assuming a vendor’s paper environment is a neutral or complete model of live markets.
| Decision area | What to verify |
|---|---|
| Assets and order types | Which instruments and order types are supported for your selected API version. |
| Market data | Feed coverage, historical depth, and account entitlements; identify the actual feed users receive. |
| Fill realism | Published fill rules and unmodeled execution effects. |
| Account and portfolio | Available account, order, and position endpoints and how their records fit your ledger. |
| Sandbox separation | How paper credentials and accounts are separated from live trading credentials and accounts. |
| Operational control | Whether you need deterministic replay, persistence, and control over simulation assumptions. |
Alpaca’s documentation provides an example of a hosted trading API and paper environment, but it does not establish a balanced comparison with other providers. Evaluate current vendor documentation for the specific features your app needs.
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Disclose the simulation in the product
Tell users that orders are simulated, name the feed and its scope, and summarize the fill assumptions that affect their results. Keep these disclosures close to order entry, fill details, and performance views so a user does not mistake simulated output for a live execution record.
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