The rest of FactorForge is built for quant researchers, so it's full of jargon. This page is the opposite: no math, no assumptions. If a word anywhere on the site looks intimidating, it's explained here — and you'll see the same dotted underlined terms across the app that you can hover for a one-line reminder.
Understand these and the dashboards stop looking scary.
A tiny slice of ownership in a company that you can buy and sell.
Why it matters: If the company does well over time, the slice tends to be worth more; if it does badly, less.
Example: Owning 1 share of Apple means you own a (very small) piece of Apple.
The short code that stands for a company on the stock market.
Example: AAPL = Apple, MSFT = Microsoft, NVDA = NVIDIA.
A standard yardstick you compare a strategy against — usually the whole market.
Why it matters: Beating a benchmark matters more than just making money: making 8% is only good if simply holding the market would have made less.
Example: SPY tracks the 500 biggest US companies, so it's a common 'the market' yardstick.
A single thing you can buy that holds a whole basket of stocks at once.
Example: Buy SPY and you effectively own a slice of 500 companies in one go.
How much you gained or lost, as a percentage of what you put in.
Example: Put in $100, end with $112 → a +12% return.
A multi-year result rephrased as 'about this much per year', so different time spans are comparable.
Why it matters: A +50% gain over 5 years isn't as good as +50% in one year — annualizing makes that obvious.
The worst peak-to-bottom drop along the way — how much you'd have been down at the scariest moment.
Why it matters: Two strategies can end at the same place, but the one that fell -40% in the middle is far harder to actually hold through.
Example: Account goes $100 → $130 → $98 → recovers. The drawdown is from $130 down to $98, about -25%.
How bumpy the ride is — how much the price jumps around day to day.
Why it matters: Higher volatility means bigger swings in both directions, so the same average return feels riskier.
Reward for the bumpiness: how much return you earned per unit of risk taken.
Why it matters: Higher is better. Roughly: under 1 is so-so, above 1 is good, above 2 is excellent. It rewards smooth gains over wild ones.
Example: Two strategies both return 10%, but the calmer one has the higher Sharpe — and is usually the better bet.
The share of trades that ended in a profit.
Why it matters: On its own it can mislead — a 40%-win strategy can still win overall if the wins are bigger than the losses.
Total money made on winning trades divided by total money lost on losing ones.
Why it matters: Above 1 means the wins outweigh the losses. The bigger, the better.
A dry run of a trading rule on past data to see how it would have done — no real money involved.
Why it matters: It's evidence, not a promise: doing well in the past does not imply the future.
A fixed set of rules for when to buy and when to sell, applied the same way every time.
Why it matters: Fixed rules remove emotion and let you test whether the idea actually works.
The idea that stocks already going up tend to keep going up for a while.
Example: A momentum rule buys recent winners rather than bargain-hunting losers.
The opposite idea: after an unusual move, prices often snap back toward their average.
Example: A pullback rule buys a strong stock that dipped, betting it bounces back.
The small gap between the price you hoped to trade at and the price you actually got.
Why it matters: Ignoring it makes a backtest look better than real life. This platform deliberately subtracts it.
Practicing with pretend money to watch how a strategy behaves live, without risking anything.
Why it matters: It's the safe step between a backtest and ever using real money. This platform never places real orders.
Tune the rule on an earlier slice of history, then test it on a later slice it has never seen.
Why it matters: It's the honest test for 'did this rule really work, or was it just fit to old data?'
Tuning a rule so perfectly to the past that it memorises old luck instead of learning something real.
Why it matters: Overfit strategies look amazing in a backtest and then fall apart on new data.
The collection of everything you hold, taken together as one combined bet.
Not putting all your eggs in one basket — spreading bets so one bad outcome doesn't sink everything.
Why it matters: The catch: things that look different can still move together, so they don't actually spread the risk.
A score from -1 to +1 for how much two things move together.
Why it matters: Near +1 = they rise and fall as one (little diversification); near 0 = unrelated; below 0 = they tend to zig when the other zags.
How many genuinely different bets you really have, after accounting for overlap.
Why it matters: Four strategies that all secretly bet on tech might only be ~1.5 real bets. This number reveals 'four bets, or one bet wearing four hats?'.
Example: 4 strategies but N_eff ≈ 1.5 → far less diversified than it looks.
The industry group a company belongs to, like Technology, Energy, or Health Care.
Why it matters: Spreading across sectors is real diversification; owning ten tech names is not.
The five numbers that summarise one day of trading: Open, High, Low, Close price, and Volume (how many shares changed hands).
Why it matters: Almost everything on this platform is computed from these daily numbers — nothing is made up.
A common driver behind many stocks' returns — like 'is it a momentum name?' or 'is it low-volatility?'.
Why it matters: Factor attribution asks: is this strategy real skill, or just riding a well-known driver anyone could buy?
Stand-in fake data used only when real market data can't be fetched.
Why it matters: It keeps the demo working, but the platform always labels it clearly so it's never mistaken for real results.
Now that the words make sense, explore the real thing. Every metric you meet has a hover explanation.
Reminder: this platform is a research and learning demo only — no live order execution, no real orders, and past backtests do not imply future results.