MachineLearningLibrary "MachineLearning"
Quantum-TA • Machine – Adaptive ML Toolkit for Pine v6
Bring modern data-science techniques to any TradingView script without external servers or heavy tensors.
This library blends low-lag filtering, regime detection, information-theory gauges …and two tiny inference-only models – a KAN (Kolmogorov-Arnold Network) and a lite Temporal-Fusion Transformer (TFT) – then lets a self-training ensemble decide which one to trust bar-by-bar.
clamp(value, minVal, maxVal)
Parameters:
value (float)
minVal (float)
maxVal (float)
q_log(x)
Parameters:
x (float)
tanh(x)
Parameters:
x (float)
fisher_volatility(src, len)
Parameters:
src (float)
len (simple int)
ema(src, len)
Parameters:
src (float)
len (int)
normalizeArray(arr)
Parameters:
arr (array)
hmm_volatility_regime(atr_current)
Parameters:
atr_current (float)
tft_model(inputVector, len, learningRate, regime_probs)
Parameters:
inputVector (array)
len (int)
learningRate (float)
regime_probs (array)
normalizeWeights(w1, w2)
Parameters:
w1 (float)
w2 (float)
final_prediction(kan_pred, attn_pred, w_kan, w_attn)
Parameters:
kan_pred (float)
attn_pred (float)
w_kan (float)
w_attn (float)
ensemble_weight_predictor(target_weight, kan_err, tft_err, atr_norm, regime_probs)
Parameters:
target_weight (float)
kan_err (float)
tft_err (float)
atr_norm (float)
regime_probs (array)
ensemble_weights(kan_err, tft_err, atr, regime_probs)
Parameters:
kan_err (float)
tft_err (float)
atr (float)
regime_probs (array)
render(source)
Parameters:
source (float)
