Backtesting & Strategy Simulation

Backtesting only works when strategies are tested against how markets actually behaved. Price candles alone smooth out liquidity, timing, and execution effects. With historical trades, quotes, order book activity, and market events, strategy simulations can replay real market conditions and reveal where a strategy truly works — and where it breaks down.
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Your challenge
Most backtests look good on paper, but fall apart in real markets.

Simplified historical data hides liquidity gaps, order flow, session changes, and trade conditions that affect execution and risk. When strategies are tested on clean price series instead of real market behavior, results become overly optimistic and fragile. Without detailed historical market data — like the trades, quotes, order book updates, and market events provided by FinFeedAPI — simulations fail to reflect how strategies behave once real market conditions are involved.

Backtests rely on oversimplified price data

Order execution is assumed, not simulated

Market timing is inaccurate

Trade conditions are ignored

Results don’t transfer to live trading

How Does FinFeedAPI Solve It?

Replay market behavior, not just price paths

FinFeedAPI’s Stock Market API includes historical trades, quotes, and order book data, allowing backtests to reflect how prices actually formed during each session. This makes simulations closer to real market behavior instead of idealized outcomes.

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Before vs After FinFeedAPI

Strategies are tested onBeforeAfter (with Stock Market API)
Price data used in simulationsOHLCV candles only. Intraperiod behavior is smoothed away.OHLCV combined with trades, quotes, and order book updates showing how prices actually formed.
Order execution assumptionsFills assumed at ideal prices with no queue or slippage effects.Level 1, Level 2, and Level 3 data allow simulations to reflect real liquidity and execution constraints.
Liquidity awarenessLiquidity inferred indirectly from volume or spread proxies.Price level and order-level data reveal how liquidity appears, shifts, and disappears over time.
Handling market sessionsPre-market, regular, and post-market often mixed together.System events clearly mark session boundaries so strategies trade in the correct market phase.
Treatment of special tradesOdd lots and extended-hours trades treated like normal activity.Trade flags identify special trade conditions so simulations can filter or label them properly.
Market interruptionsHalts and auctions appear as unexplained gaps or noise.Admin messages explain halts, auctions, and status changes at the symbol level.
Strategy robustnessBacktests look stable but break under live conditions.Simulations expose weaknesses early by reflecting real market complexity.
Backtesting workflowsCustom data stitching and fragile tooling.One consistent Stock Market API, usable via REST or JSON-RPC, for repeatable simulations.

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FAQ: Backtesting & Strategy Simulation & Stock Market API
What data is required for realistic stock market backtesting?

Realistic backtesting requires more than OHLCV candles. FinFeedAPI provides historical trades, quotes, order book updates, system events, and market status messages, allowing simulations to reflect how markets actually behaved during different conditions and sessions.

Why do many trading strategies fail after strong backtest results?

Many strategies are tested on simplified data that ignores liquidity constraints, execution risk, and market state changes. FinFeedAPI helps reduce this gap by offering detailed historical market data that exposes the complexities strategies face in live trading.

How does order flow data improve strategy simulation results?

Order flow data reveals how buying and selling pressure evolves over time. With Level 2 and Level 3 order book data from FinFeedAPI, simulations can account for changing depth, order cancellations, and execution timing instead of relying on static assumptions.

Why are market sessions and halts important in backtesting?

Market behavior differs between pre-market, regular sessions, post-market, and during halts. FinFeedAPI includes system events and admin messages that clearly mark these phases, allowing strategies to be tested within the correct market context.

Is OHLCV data alone enough to evaluate trading strategies?

OHLCV data shows price outcomes but hides how those prices were reached. FinFeedAPI complements OHLCV with trades, quotes, order book activity, and market events, leading to more realistic simulations and more reliable strategy evaluation.

How does FinFeedAPI improve stock market backtesting accuracy?

FinFeedAPI improves backtesting by providing historical stock market data that reflects real trading behavior, not just price outcomes. Alongside OHLCV, it includes trades, quotes, order book updates, system events, and admin messages. This allows strategies to be tested under realistic liquidity, timing, and execution conditions, helping uncover risks that simple price-based backtests often miss.

What data does FinFeedAPI provide for strategy simulation?

FinFeedAPI’s Stock Market API offers historical trades with detailed flags, Level 1 quotes, Level 2 price level updates, Level 3 order book events, OHLCV data, system events, and symbol-level admin messages. Together, this data supports deeper simulations that account for order flow, market state changes, and execution dynamics.

Why is order book data from FinFeedAPI important for backtesting strategies?

Order book data from FinFeedAPI shows how liquidity forms, shifts, and disappears over time. Level 2 and Level 3 data allow simulations to consider queue position, price impact, and slippage, which are critical for evaluating how a strategy would behave in real market conditions.

Can FinFeedAPI be used to simulate real trade execution conditions?

Yes. By combining trades, quotes, and order book events, FinFeedAPI enables strategy simulations that reflect realistic execution scenarios. This helps model fills, partial executions, and changing liquidity instead of assuming perfect execution at ideal prices.

How does FinFeedAPI support large-scale backtesting workflows?

FinFeedAPI delivers all historical stock market data through a consistent API, available via REST and JSON-RPC. This makes it easier to build repeatable backtesting pipelines, replay historical sessions, and compare strategy performance across different market periods.