A glossary of quantitative trading and strategy-validation terms — backtesting, overfitting, walk-forward testing, Monte Carlo simulation, drawdown, and more — as used in Maple Research.
Backtest
A simulation of how a trading strategy would have performed if it had been applied to historical market data. Maple's backtests are deterministic — the same strategy and data always produce the same result.
Overfitting
When a strategy is tuned so closely to a specific historical dataset that it captures random noise rather than a real, repeatable pattern, producing backtest results that look strong but fail to hold up on new data. Maple's validators are built specifically to detect this.
Out-of-sample testing
Evaluating a strategy on data it was not built or tuned on, to see whether its performance holds up outside the exact conditions it was designed around.
In-sample data
The portion of historical data used to build or tune a strategy, as opposed to out-of-sample data used to independently check it afterward.
Walk-forward testing
A validation method that re-tests a strategy across a rolling series of time windows rather than one fixed historical period, checking whether its edge persists as market conditions change.
Monte Carlo simulation
A technique that reorders or resamples a strategy's trades many times to see how much its results depend on the specific sequence events happened to occur in, rather than the underlying rule.
Drawdown
The decline in a strategy's account value from a previous peak, usually expressed as a percentage. Maximum drawdown is one of the most important risk measures in evaluating a strategy.
Maximum drawdown
The largest peak-to-trough decline a strategy experienced over a given backtest period — a key measure of how much loss a strategy could realistically require an investor to tolerate.
Sharpe ratio
A measure of risk-adjusted return: the average return of a strategy relative to its volatility. A higher Sharpe ratio suggests more return was earned per unit of risk taken.
Sortino ratio
A variation on the Sharpe ratio that only penalizes downside volatility (losses), rather than all volatility, on the reasoning that upside volatility isn't the risk investors actually care about.
Win rate
The percentage of a strategy's trades that were profitable. A high win rate does not by itself indicate a good strategy — it must be considered alongside average win/loss size.
Risk-reward ratio
The ratio between a trade's potential loss and its potential gain, often used to size positions and evaluate whether a strategy's trade structure makes statistical sense.
Sample size
The number of trades or data points a backtest is based on. A strategy with too few trades may show a statistically unreliable result, even if the win rate looks strong.
Statistical significance
A measure of how likely it is that a result (such as a strategy's apparent edge) reflects a real effect rather than random chance, given the amount of data available.
Parameter sensitivity
How much a strategy's results change when its settings (like a moving-average length or a stop-loss level) are adjusted slightly. Strategies that only work with one very specific parameter combination are considered fragile.
Curve fitting
Another term for overfitting: adjusting a strategy's rules or parameters until they match historical data as closely as possible, at the cost of real-world reliability.
Market regime
A distinct set of market conditions — such as a strong trend, a range-bound period, or high volatility — that can significantly affect how a strategy performs.
Regime dependence
When a strategy's apparent edge is concentrated in one type of market condition and largely absent in others, meaning its historical average return may not reflect future conditions.
Slippage
The difference between a trade's expected execution price and its actual execution price, often caused by market movement between the decision to trade and the order being filled.
Transaction costs
Fees, spreads, and other costs incurred when executing trades, which reduce a strategy's real-world returns relative to its theoretical backtest performance.
Equity curve
A chart showing how a strategy's account value changes over time across a backtest or live trading period — one of the most direct ways to see a strategy's overall behavior and drawdown pattern.
Alpha
In quantitative finance, the portion of a strategy's return that cannot be explained by general market movement — in other words, a genuine, skill- or edge-based excess return.
Beta
A measure of how sensitive a strategy or asset's returns are to overall market movements. A beta of 1 means it tends to move in line with the market.
Volatility
A statistical measure of how much an asset's price fluctuates over time. Higher volatility generally implies higher risk and larger potential price swings.
Mean reversion
A trading approach based on the idea that prices tend to return toward an average level after moving away from it, used as the basis for many contrarian strategy designs.
Trend following
A trading approach that seeks to profit from sustained directional price movement, typically entering positions in the direction of an established trend.
Moving average
The average price of an asset over a set number of past periods, commonly used to smooth out short-term price noise and identify trend direction.
Moving average crossover
A common signal generated when a shorter-term moving average crosses above or below a longer-term moving average, often used as an entry or exit trigger.
Momentum
The tendency of an asset that has been moving in one direction to continue moving in that direction for some period, forming the basis of momentum-based strategies.
Volatility filter
A rule added to a strategy that adjusts or restricts trading based on current market volatility, often used to avoid entering trades during unusually erratic conditions.
Stop-loss
A predefined price level at which a losing position is automatically closed, used to limit downside risk on an individual trade.
Take-profit
A predefined price level at which a winning position is closed to lock in gains, used alongside a stop-loss to define a trade's risk-reward structure.
Position sizing
The process of deciding how much capital to allocate to a given trade, a major factor in a strategy's overall risk profile independent of its entry/exit rules.
Paper trading
Simulated trading using virtual capital rather than real money, used to observe how a strategy behaves against live market prices without financial risk.
Historical data
Recorded past market prices and related information used as the input for backtesting a strategy.
Timeframe
The interval each data point in a chart or backtest represents — for example, 15-minute, 1-hour, or daily candles — which can significantly affect a strategy's behavior.
Candlestick
A chart element representing price movement over a given period, showing the open, high, low, and close prices for that interval.
Strategy
In Maple, a defined, testable set of rules for entering and exiting positions on a given asset and timeframe.
Confidence score
Maple's summary signal for how much statistical weight a backtest result can reasonably bear, based on its validator results — not a prediction of future returns.
Validator
One of Maple's independent checks (such as out-of-sample testing or parameter sensitivity) run against a backtest result to assess its robustness.
Autonomous research
Maple's mode for testing many strategy variations on an asset automatically and surfacing the strongest, most robust candidates, without requiring any AI model.
Quantitative research
The practice of using data, statistics, and systematic testing — rather than intuition alone — to evaluate trading ideas and strategies.
Algorithmic trading
Trading based on predefined, rules-based logic rather than discretionary human decision-making at the moment of each trade.
Robustness
How consistently a strategy performs across different time periods, parameter settings, and market conditions, as opposed to performing well only in one narrow, specific case.
Edge
A real, repeatable statistical advantage a strategy has over random chance — the thing quantitative research is ultimately trying to identify or rule out.
Survivorship bias
A distortion that occurs when a dataset only includes assets or entities that still exist today, leaving out those that failed or were delisted, which can make historical results look better than they really were.
Look-ahead bias
A backtesting error where a strategy is accidentally given access to information that would not have been available at the time a real trade decision was made, inflating its apparent performance.
Data snooping
Testing many strategy variations against the same dataset until one appears to work, without accounting for the fact that some will look good by chance alone — a major driver of overfitting.
Correlation
A statistical measure of how closely two assets or variables move in relation to one another, often used to understand diversification or shared risk across strategies.
Diversification
Spreading exposure across multiple assets or strategies so that no single position or approach dominates overall risk.
Risk management
The set of practices used to control how much a trader or strategy stands to lose, including position sizing, stop-losses, and diversification.
Backtesting engine
The underlying software system that simulates a strategy's trades against historical data and computes its performance statistics. Maple's engine is deterministic and reproducible.
Deterministic (in computing)
Producing the exact same output every time given the exact same input — a property Maple's core research engine is specifically built to have, unlike probabilistic AI-generated content.
Cryptocurrency market
A market for digital assets such as Bitcoin and Ethereum, which Maple supports for backtesting and paper trading alongside traditional equity markets.
Equities
Shares of publicly traded companies, one of the asset classes Maple supports for strategy research alongside cryptocurrency.
Bull market
An extended period of generally rising asset prices, one of the market regimes a strategy's performance can depend heavily on.
Bear market
An extended period of generally falling asset prices, another distinct market regime that can significantly affect strategy performance.
Liquidity
How easily an asset can be bought or sold without significantly moving its price — low liquidity can make real-world execution meaningfully worse than a backtest suggests.