Overall Statistics
Total Orders
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Start Equity
100000
End Equity
100000
Net Profit
0%
Sharpe Ratio
0
Sortino Ratio
0
Probabilistic Sharpe Ratio
0%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
0
Tracking Error
0
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
Portfolio Turnover
0%
 
# region imports
from AlgorithmImports import *
# endregion

class SquareRedOrangeHyena(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2023, 5, 17)

        input_text = "Apple's AI-Powered Growth To Trigger 10% Revenue Surge By 2025, Predicts Gene Munster: 'This Is Just The Start Of Apple's Next Growth Chapter', Apple is poised for a significant growth spurt, driven by its artificial intelligence capabilities, according to Gene Munster, a prominent tech analyst"

        import tensorflow as tf
        from transformers import TFBertForSequenceClassification, BertTokenizer

        model_name = "ProsusAI/finbert"
        tokenizer = BertTokenizer.from_pretrained(model_name)
        model = TFBertForSequenceClassification.from_pretrained(model_name, from_pt = True)

        inputs = tokenizer(input_text, return_tensors="tf")
        outputs = model(**inputs)

        # Extract logits
        logits = outputs.logits
        # Convert logits to probabilities using softmax
        probabilities = tf.nn.softmax(logits, axis=-1)
        # Determine predicted labels
        predicted_labels = tf.argmax(probabilities, axis=-1)

        self.quit(f"Probabilities: {probabilities.numpy()} Predicted Labels {predicted_labels.numpy()}")