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…
Bill Williams fractals — five-bar pivots where the middle bar’s high is highest (or low is lowest) — are a clean structural primitive but useless on their own as a trade signal. BigBeluga’s Fractals Trend takes the next step: store the most recent N fractal highs and N fractal lows, smooth them into trend bands,…
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…
capissimo’s Chop and explode (ps5) reframes the classic RSI as a regime detector. Instead of reading 70/30 thresholds for overbought/oversold, this version applies RSI to a minimax-scaled source — the price is normalized to a 0-100 range over a window — and uses 60/40 as the BUY/SELL switches. When the scaled RSI sits between 40…
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…
Most SuperTrend variants are deterministic: ATR × multiplier sets the band width and the trend flips when price closes the wrong side. LuxAlgo’s KNN Supertrend Horizon takes a different swing — it runs a k-nearest-neighbors classifier over recent (RSI, ATR%) feature space, votes on the next-bar SuperTrend direction, EMA-smooths the probability, and only flips when…
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…
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