NinjaScript tutorials from someone who’s been writing it since NT6.5. Start at the top if you’re new — or jump to whatever you’re stuck on.
You have an SMA. You want to know when price crosses it — that’s a signal. You also want to know when the SMA itself changes direction — that’s a different signal, same class of problem. Both come down to comparing current and previous values, and NinjaScript has built-in helpers for the common case plus…
The previous post built a vanilla Recurrent Neural Network — a model with a hidden state that carries forward bar to bar — and then ran straight into its defining limitation. Training a recurrent network means multiplying the gradient by a tanh derivative at every step back through time, and a long chain of numbers…
When you call AddPlot, NinjaScript creates a Series<double> behind the scenes that you access through Values[i]. That’s a custom Series with nothing special done to it — it’s the same type you can create for your own use when you need per-bar tracking that doesn’t belong on a plot. This post covers when to reach…
Every model in this series so far — k-Nearest Neighbors, online logistic regression, the single– and multi-hidden-layer nets, and last post’s Adam-trained version — has shared one quiet limitation. Each prediction sees only the current bar’s feature vector. If a pattern depends on what the market did five bars ago, that information has to be…
An SMA cross that fires at 3 a.m. on overnight NQ is rarely useful. A reversal setup at 11:58 during the lunch chop is worse than useless. Most trading logic belongs in specific windows, and NinjaScript has simple tools for keeping it there — a couple of integer comparisons and a helper method you write…
The prior post let you stack any number of hidden layers — but every weight update was still plain stochastic gradient descent: one update per bar, one fixed step size for every weight. Plain SGD is the simplest possible training rule, and it works on shallow networks, but two things tend to go wrong at…
Tooltips live in two places in a NinjaScript indicator. The first is the settings dialog — the small hover text that explains what a property does. The second is the chart itself — labels and annotations attached to drawn objects that show context when a trader hovers over them. Both are cheap to add, both…
The prior post built a single-hidden-layer neural network — three inputs, six tanh neurons in one hidden layer, one sigmoid output, 31 parameters. It worked because the hidden layer can compose the raw features into non-linear combinations that a single neuron cannot represent. The post closed with a hint: “add a hidden layer + backprop…
A polished indicator hides settings that don’t apply to the current state. When a ShowLabels bool is off, the LabelColor, LabelSize, and LabelFont properties should disappear from the grid — they’re irrelevant unless labels are enabled. Standard NinjaScript attributes don’t give you that directly. There’s no [Display(EnabledWhen = “ShowLabels”)]. Three workarounds do it. Each has…
The prior post built a single-neuron model — three weights, a bias, and a sigmoid that turned a weighted sum into P(up). It worked, but its limits were architectural: a single neuron can only carve the feature space with a straight line. Any pattern that requires the features to interact non-linearly — “high distFromMa AND…
Notifications
