PaperTrader Documentation
PaperTrader is a free market research and educational tool that lets you practice investing without risking real money. Whether you’re brand new to the stock market or looking to sharpen your skills, PaperTrader gives you a safe sandbox to learn how trading works, experiment with different strategies, and study how those strategies would have performed on real historical data.
There are two main ways to use the app:
- Paper Trading — Build a virtual portfolio by buying and selling stocks, ETFs, and cryptocurrencies at real market prices, all with play money.
- Backtesting — Pick a trading strategy, choose a stock and date range, and let the engine simulate every trade that strategy would have made in the past. You get a full breakdown of returns, risk, and individual trades.
No financial background is required. Every concept is explained below.
What is Paper Trading?
Paper trading is simulated investing. You start with a virtual cash balance and place buy or sell orders on real assets — stocks like AAPL (Apple), ETFs like SPY (S&P 500), or cryptocurrencies like BTC/USD — just as you would with a real brokerage account. The only difference is that no actual money changes hands.
The term dates back to when aspiring traders would jot hypothetical trades on a piece of paper and track how they played out. PaperTrader automates that entire process: it records every order, tracks your holdings, calculates your profit and loss, and shows your portfolio’s value in real time.
Why Paper Trade?
- Zero financial risk — Make mistakes and learn from them without losing a cent.
- Real market data — Prices come from live markets, so the experience mirrors real trading.
- Build intuition — Develop a feel for how prices move, how different asset classes behave, and how your decisions affect returns.
- Practice discipline — Learn to manage a portfolio, set price targets, and decide when to cut losses.
What is Backtesting?
Backtesting answers a simple but powerful question: “If I had followed this exact trading strategy over the past X months or years, how would I have done?”
The engine replays historical price data day by day. Each time the strategy’s rules are triggered — for example, a moving average crossover — it places a simulated trade at the price that was actually available on that date. At the end, you get a detailed report: total return, biggest loss, a chart of your portfolio’s growth, and a log of every trade.
Why Backtest?
- Learn strategy mechanics — See exactly how a strategy decides when to buy and sell.
- Compare approaches — Run the same date range with different strategies or parameters and compare outcomes side-by-side.
- Understand risk — Metrics like maximum drawdown reveal the worst dip your portfolio would have experienced.
- Spot weaknesses — A strategy might thrive in a bull market but crumble during a crash. Backtesting surfaces those patterns.
Important: Past performance does not guarantee future results. Backtesting is a learning and research tool — it helps you eliminate bad ideas and understand market dynamics, but markets can always behave differently in the future.
Getting Started
Creating an Account
- Click “Log in” in the navigation bar, then choose “Register” to create a new account with your email and a password. You can also sign in with Google.
- Once logged in, you have access to your own private portfolio and backtest history.
Paper Trading
- Open the Dashboard to view your portfolio, cash balance, and current positions.
- To make a trade, click a ticker or use the trade form:
- Search for a ticker symbol (e.g., AAPL, MSFT, BTC/USD).
- Choose Buy or Sell.
- Enter the number of shares or units.
- Confirm the order.
- Your positions, average cost basis, and unrealized profit/loss update automatically.
- Visit the Trades page for a complete history of every order you have placed.
Running a Backtest
- Navigate to the Backtest page and click “New Backtest.”
- Fill out the backtest form:
- Name — A label for this run (e.g., “AAPL SMA 10/50 2024”).
- Strategy — Select from the strategies described above.
- Parameters — Adjust the strategy-specific settings or keep the defaults.
- Tickers — Search and add one or more ticker symbols. Some strategies (Dual Momentum) require at least two.
- Date range — The historical period to simulate.
- Initial capital — The virtual starting balance (default $10,000).
- Click “Run” and wait for the engine to process. Large date ranges may take a few seconds.
- Click the completed backtest to open the results dashboard with performance metrics, an equity curve, strategy-specific indicator charts, and a full trade log.
Backtesting Strategies
PaperTrader includes seven built-in strategies ranging from classic technical indicators to passive benchmarks. Each strategy is described below with its underlying logic, configurable parameters, and guidance on when it tends to work well (or poorly). All strategies support single-ticker and multi-ticker backtests.
SMA Crossover
The Simple Moving Average (SMA) Crossover is one of the most widely-taught trend-following strategies. It uses two moving averages — a fast one and a slow one — to detect shifts in a stock’s trend.
How it works
An SMA is just the average closing price over the last N days. The “short” SMA (default 10 days) reacts quickly to price changes, while the “long” SMA (default 50 days) smooths out noise and shows the broader trend.
- Buy signal: The short SMA crosses above the long SMA. This suggests the recent trend is turning upward.
- Sell signal: The short SMA crosses below the long SMA. This suggests momentum is fading.
Parameters
| Parameter | Default | Description |
|---|---|---|
| Short Period | 10 | Number of days for the fast moving average |
| Long Period | 50 | Number of days for the slow moving average |
When to use
SMA Crossover works best in markets with clear trends. In choppy, sideways markets it can generate false signals (frequent buy/sell whipsaws). Try widening the gap between the short and long periods to filter out noise, or narrowing it to react faster.
EMA Crossover
The Exponential Moving Average (EMA) Crossover works on the same crossover principle as SMA, but uses exponential moving averages instead.
How it works
An EMA gives more weight to recent prices, so it reacts faster to new information than an SMA of the same length. The trade-off is that it can also be more sensitive to short-term noise.
- Buy signal: Short EMA crosses above the long EMA.
- Sell signal: Short EMA crosses below the long EMA.
Parameters
| Parameter | Default | Description |
|---|---|---|
| Short Period | 10 | Number of days for the fast EMA |
| Long Period | 50 | Number of days for the slow EMA |
When to use
Choose EMA Crossover when you want earlier entry and exit signals compared to SMA. It tends to outperform SMA in fast-moving markets, but can produce more false signals during low-volatility stretches.
RSI (Relative Strength Index)
The RSI is a momentum oscillator that measures how quickly and how far prices have moved recently. It produces a value between 0 and 100.
How it works
RSI compares the average size of recent up-moves to the average size of recent down-moves over a lookback period (default 14 days). When the RSI drops below the oversold threshold (default 30), it suggests the asset may be undervalued and due for a rebound. When it rises above the overbought threshold (default 70), it suggests the asset may be overextended and due for a pullback.
- Buy signal: RSI falls below the oversold threshold.
- Sell signal: RSI rises above the overbought threshold.
Parameters
| Parameter | Default | Description |
|---|---|---|
| Period | 14 | Lookback window for the RSI calculation |
| Oversold Threshold | 30 | RSI level that triggers a buy |
| Overbought Threshold | 70 | RSI level that triggers a sell |
When to use
RSI is a mean-reversion strategy — it bets that extreme moves will reverse. It works well in range-bound or gently trending markets. In a strong sustained trend, RSI can signal “overbought” long before the rally ends, causing you to sell too early.
MACD (Moving Average Convergence Divergence)
MACD is a trend-following momentum indicator that shows the relationship between two exponential moving averages of different lengths.
How it works
The MACD line is the difference between a fast EMA (default 12 days) and a slow EMA (default 26 days). A separate “signal line” — an EMA of the MACD line itself (default 9 days) — is used to generate trade signals.
- Buy signal: The MACD line crosses above the signal line. This indicates that short-term momentum is accelerating relative to the longer-term trend.
- Sell signal: The MACD line crosses below the signal line.
Parameters
| Parameter | Default | Description |
|---|---|---|
| Fast Period | 12 | Days for the fast EMA |
| Slow Period | 26 | Days for the slow EMA |
| Signal Period | 9 | Days for the signal line EMA |
When to use
MACD combines trend detection with momentum measurement and is one of the most versatile indicators. It works best in trending markets. Like all moving-average-based strategies, it lags behind price action, so signals arrive after a move has already started.
Bollinger Bands
Bollinger Bands wrap a moving average with an upper and lower band based on standard deviation, creating a dynamic “envelope” around the price.
How it works
The middle band is an SMA (default 20 days). The upper and lower bands are placed a set number of standard deviations (default 2.0) above and below that SMA. When the price touches the lower band, the asset is considered relatively cheap; when it touches the upper band, it is considered relatively expensive.
- Buy signal: The closing price drops to or below the lower band (potential bounce).
- Sell signal: The closing price rises to or above the upper band (potential pullback).
Parameters
| Parameter | Default | Description |
|---|---|---|
| Period | 20 | SMA lookback window (middle band) |
| Std Dev Multiplier | 2.0 | Number of standard deviations for the bands |
When to use
Bollinger Bands implement a mean-reversion approach. They shine in range-bound markets where prices oscillate between support and resistance. In a strong breakout, prices can “ride the band” for extended periods, leading to premature sell signals.
Buy and Hold
Buy and Hold is the simplest possible strategy and serves as a useful benchmark. It answers: “What if I just bought on day one and held until the end?”
How it works
On the first day of the backtest, the engine invests all available capital equally across the selected tickers and holds those positions until the end date. No further trades are made.
Parameters
None. This strategy has no configurable settings.
When to use
Run a Buy and Hold backtest alongside any other strategy to see whether the active strategy actually added value. If a complex strategy cannot beat simple buy-and-hold over the same period, that is valuable information.
Dual Momentum
Dual Momentum is a rotational strategy that periodically shifts capital into whichever asset has the strongest recent performance — but only if that performance is positive. It requires at least two tickers to compare.
How it works
At the end of each month, the engine looks back over a configurable window (default 63 trading days, roughly three months) and calculates the return of each ticker over that window. It then moves 100% of the portfolio into the ticker with the highest positive return. If no ticker has a positive return, the portfolio moves to cash until the next rebalance.
- Relative momentum: Among the selected tickers, which one performed best?
- Absolute momentum: Was that best performer actually positive? If not, stay in cash.
Parameters
| Parameter | Default | Description |
|---|---|---|
| Lookback Period | 63 | Number of trading days to measure momentum |
When to use
Dual Momentum is designed for investors comparing multiple assets — for example, a stock index vs. a bond index vs. gold. It aims to capture uptrends while avoiding prolonged downturns by moving to cash. It requires a multi-ticker backtest (add at least two tickers).
Reading Your Results
After a backtest completes, PaperTrader displays a results dashboard with key performance metrics. Here is what each one means:
Total Return
The overall percentage gain or loss from start to finish. For example, a total return of +15.4% on $10,000 initial capital means the portfolio ended at $11,540.
Max Drawdown
The largest peak-to-trough decline during the backtest, expressed as a percentage. If your portfolio grew to $12,000 and then dropped to $10,200 before recovering, the max drawdown is -15%. This metric captures the worst-case pain you would have experienced.
Sharpe Ratio
A measure of risk-adjusted return. It divides the average daily return by the volatility (standard deviation) of those returns, then scales to an annual figure. A higher Sharpe ratio means you earned more return per unit of risk. As a rough guide:
- Below 0 — The strategy lost money.
- 0 to 1 — Positive but low risk-adjusted returns.
- 1 to 2 — Good risk-adjusted returns.
- Above 2 — Excellent (and rare over long periods).
Win Rate
The percentage of completed (buy then sell) trades that ended in a profit. A 60% win rate means 6 out of every 10 round-trip trades were profitable. Note that a high win rate alone does not guarantee profitability — a strategy can win often but lose big on the losses.
Equity Curve
The chart plotting your portfolio’s value day by day throughout the backtest. A steadily rising curve suggests consistent performance; large dips reveal drawdown periods. Trade markers on the chart show exactly when the strategy bought and sold.
Trade Log
A table listing every simulated trade: the date, ticker, side (buy or sell), quantity, and execution price. Use it to understand why the strategy acted at a given point — cross-reference with the strategy chart to see the indicator values that triggered the trade.