You should use Equities Lab if…
You Teach Investing
Make your curriculum come alive by having your students interact with real market data.
You Teach Portfolio Analysis
Diversification, leverage, long/short portfolios, and factor analysis, in seconds.
You Advise a Student Managed Fund
Quickly design and implement alpha-generating strategies. Validate and track results.
Equities Lab Overview
Teaching quantitative investment is hard. You have numerous complex topics to cover, but you have to do it in a way that’s engaging. If you just give your students a series of lectures and worksheets, many won’t actually absorb the material. Equities Lab makes teaching easy, especially if time is limited.
Students interact with a rich interface to demonstrate their understanding of quantitative concepts. They get immediate feedback with Equities Lab’s auto-graded homework system, which gives them space to play and learn.
Multiple semesters of work can be fit into one, as students don’t have to find, manage, clean, compact, and wrangle masses of data, and get quantitative backtesting algorithms exactly right. You can demonstrate even complex features easily, and engage them with it as part of their coursework, either in projects, simulated portfolios, or homework.
Explore our academic features
Virtual Stock Market Competitions
- Students buy and sell virtual stocks using real-time prices
- Allow students to put in orders for next market open/close
- Restrict stocks that students can invest in
- For example, no penny stocks or no mega-caps or only indexes
- All classroom portfolios are gathered into overview tables with metrics
- Sort using common or custom metrics
- Run multi-portfolio reports on the whole class
- Examine and grade each portfolio individually
- Export grades and metrics into Excel by student id
- Administer student portfolios
- For example, modify student trades as needed
- No limits on the number of trades
- See your students’ portfolios as they were on any day in the past or over any covered time span
Auto-Graded Interactive Assignments
Built-in Market Anomalies
Some of our most popular anomalies and formulas
- Fama-French Factors
- P/E, P/S, P/FCF, Price/Whatever
- EBITA/EV and variants
- PEG ratio
Safety and Fraud detection
- Piotroski F-score
- Altman Z-score
- Beneish M-score
- Ohlson O-score
- SMA, DMA and EMA
- Ichimoku clouds
More than 1,000 built-in strategies, all searchable
- More than 500 public formulas
- More than 500 public screeners
- More thinking, synthesis, and analysis in your student reports
- Combine valuation and momentum
- Even complex formulas like AQR’s Quality Minus Junk can be easily modified and adapted for your use case
What’s going on here? Over this backtest, the energy stocks (bottom left) vastly outperformed the rest, driving the performance of this portfolio.
Do yield curves matter? See the linear correlation of performance vs. yield curve steepness: the steeper the curve, the better the performance.
The whole is greater than the sum of its parts. This strategy uses momentum, safety scores, valuation, and income.
You can see each subcomponent tested along with the set of stocks that match all of the tests. Notice the handy stats and bar charts breaking down performance year by year.
See what works in your portfolio and what doesn’t. This disaster of a screen failed almost everywhere, except for industrials and energy.
Some of the many metrics you can include in your homework assignments and portfolio reports…
- Best/Worst position
- Best/Worst month
- Maximum drawdown
- Conditional value at risk
- Trailing performance
- Annualized performance
- Alpha and beta
- Sharpe ratio
- Average number held
- Positions by percentile
- Months by percentile
- Odds by month of outperformance
- Odds by position of outperformance
Clean and Reliable Data
Leverage Morningstar Data
Morningstar is an industry-leading data provider that supplies Equities Lab with deep, comprehensive financials on American, Canadian, and Australian equities. This allows us to simulate, with splits and dividends handled correctly, almost any trading strategy involving technical or fundamental criteria.
- 25,000+ companies, alive and dead
- 1,000+ financial data points, from AccountsPayable, AdditionalPaidInCapital and OtherIntangibleAssets through WorkInProgress to fields like UnbilledReceivables and UnearnedIncome
- Data going back to 1995
Leverage Macroeconomic Data
You can use any data series in the Federal Reserve of St Louis data set (FRED) to help your analysis.
- Cut and paste the URL into Equities Lab, letting you use it right away
- Browse our curated list of useful FRED indicators
- Use our custom search to find the right data series
You can also use Quandl data the same way. Fair warning though: most of the good feeds from Quandl now require extra fees.
Leverage Data With Confidence
QCOR “pioneered” the process of buying up cheap generic drugs (Acthar as an example) that people depended on, ensuring no one else could produce them, and hiking the price beyond all reason. Now that QCOR is a part of Mallinckrodt, this chart is hard to find, though you can find the FTC settlement.
Perhaps appropriately, Mallinckrodt is now emerging from Chapter 11 bankruptcy, because of its behavior in the Opioid crisis, making this chart even more challenging to find.
File dates matter (really, really matter)
Why do so many people believe that fundamentals don’t matter and strategies based on them will not work in the real world?
One of the most common reasons is because so many systems will apply the new quarterly data the day after the quarter closes, which is simply not possible to do without time travel or a heavy investment in insider trading sources.
Financial results take time to come out and be processed; neglecting these delays is a good way to ruin your analysis. Equities Lab correctly delays financials, so you don’t need a crystal ball to replicate your strategies.
Don’t invest in the uninvestable
Microcaps, unloved and underanalyzed, can be a great place to invest. That said, microcaps and other low volume stocks tend to be almost impossible to enter or exit.
We therefore have configurable filters that filter out stocks that are too thinly traded, too tiny, or otherwise too hard to invest in. These stocks don’t show up in any rankings, backtests, or screens.
We also include user-configurable trading cost calculators if you wish for your students to dip their toes in those waters. Since the universe filter is configurable, you can loosen or tighten it as desired.
The perils of adjusted Close
Using the standard deviation of Close in your formula seems safe, doesn’t it? Well, it’s not that simple…
Consider the chart above where the green screener line seems to compete well with the brown S&P 500 line. This strategy supposedly wins by taking the lowest quartile of standard deviation of Close. The red “reality” line is the much smoother, albeit much sadder line you get when using Raw Close instead.
Equities Lab will warn you every time your strategy uses adjusted fields in an unsafe way and provides a plethora of properties, such as RawClose and RawOpen, to avoid such pitfalls.
Powerful Portfolio Handling
Configurable trading costs
In real life, trading costs vary per stock and per time, and can critically depend upon the details of your brokerage.
We model trading costs as slippage, which can be configured with a formula that can refer to any attribute of the security being traded. Set Market Cap cutoffs (as the Kini Trading Costs do), use average trading volume, or a composite based on your own specific needs.
This piece of seasonal insanity takes a perfectly ordinary composite of a Beneish/Piotroski/Momentum strategy and overlays it with a seasonal refusal-to-hold during the summer months and certain bad holidays.
The really interesting thing here is that the screen rebalances every time we go from a vacation state to a non-vacation state. This happens several times a year, at irregular intervals. The red reality line is merely the screen itself outperforming the market.
Constrained holding periods
Investors are human. They won’t generally jump out of a position they just jumped into, and they may also refuse to buy something they previously sold that year.
Do these quirks make a difference? Find out! The grid above shows a list of trades where the investor trades quarterly, is unable to sell for at least one year, is unwilling to hold for more than three years, and never buys a stock that has been sold. As might be expected, this underperforms vs. the base strategy, which has outperformed since 2010.
Stop-losses or stop profits?
Stop-losses may reduce volatility, but they also impact performance. In the chart above, up until 2010, the green backtest line used a 20% stop-loss.
From 2010 onwards, the strategy is much stricter: a 10% trailing stop-loss.
The green backtest line follows the orange baseline until 2010, whereupon it just seems to run out of steam. Perhaps something is “stopping” it?
The backtest tilt in this case shows an alternative view of a long/short portfolio. The large number of points clustered around the y-axis indicates that the strategy made a bold move, up or down, when the market went nowhere.
The dots around the x-axis tell a tale of drifting while the market was strongly in motion. As with many long/short strategies, market correlation is almost absent: beta is 0.007, which allows this strategy to have 2.25% alpha even while only going up half as much as the market.
Think your portfolio is too big? Why not limit the number of stocks held? Here, max holdings has been set to 5, with the ranking factor being Income Statement Score – Balance Sheet Score.