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)
Ai-trading
Market Cycles
The Market Cycles indicator transforms market price data into a stochastic wave, offering a unique perspective on market cycles. The wave is bounded between positive and negative values, providing clear visual cues for potential bullish and bearish trends. When the wave turns green, it signals a bullish cycle, while red indicates a bearish cycle.
Designed to show clarity and precision, this tool helps identify market momentum and cyclical behavior in an intuitive way. Ideal for fine-tuning entries or analyzing broader trends, this indicator aims to enhance the decision-making process with simplicity and elegance.
