Anticipate market movements with precision. Powered by a 3-Layer LSTM architecture trained on historical data since 2018 to deliver real-time price predictions and trading signals.
ABOUT
Empowering your crypto strategy with precise, data-driven Bitcoin price predictions powered by an optimized LSTM architecture.
Trained on a robust dataset of over 3,000 daily records sourced directly from the Binance API. It covers extreme market trends from 2018 to 2026, ensuring the model effectively learns from both bullish and bearish cycles.
Engineered with a specialized 3-Layer Long Short-Term Memory (LSTM) network. It is specifically designed to capture long-term dependencies, filter market noise, and accurately forecast highly volatile price movements.
Rigorously evaluated using RMSE and MAPE metrics. The model surpasses standard benchmarks by achieving an exceptional Mean Absolute Percentage Error (MAPE) of under 3%, delivering highly reliable projections.
TECHNOLOGY
To handle Bitcoin's extreme volatility, raw data undergoes strict Scaling Normalization to a 0–1 range. It is then structured using a specific windowing technique, enabling the model to learn sequential market patterns effectively.
Developed using TensorFlow and PyTorch, this specialized LSTM architecture is engineered to capture long-term market dependencies and filter out unwanted noise. The model is rigorously optimized using Mean Squared Error (MSE).
Auto-generates market analysis and SELL/BUY/HOLD recommendation based on predicted trend, momentum, and historical volatility metrics.
Real-time BTC/USD fetched from CoinGecko public endpoint every 30 seconds. Graceful fallback to dataset price if API is unreachable.
# Dataset: Kaggle (novandraanugrah)
# BTC Binance 2018–2026 | 3,007 rows
# Download: kaggle.com/datasets/
# novandraanugrah/bitcoin-historical
# -datasets-2018-2024
model = Sequential([
LSTM(100, return_sequences=True),
Dropout(0.2),
LSTM(100, return_sequences=True),
Dropout(0.2),
LSTM(50, return_sequences=False),
Dropout(0.2),
Dense(25, activation='relu'),
Dense(1)
])
model.compile(optimizer='adam',
loss='mean_squared_error')
# MAPE: 2.74% | R²: 0.9476
# Accuracy: 97.26%FEATURES
Explore the technical innovations and analytical tools powering our predictive Bitcoin dashboard.
Compare historical market trends directly with predicted prices through a highly responsive and intuitive dashboard powered by Chart.js.
Built with reliability in mind, utilizing strict RMSE and MAPE evaluations to minimize forecasting errors during high market volatility.
Employs a streamlined pipeline that extracts Python modeling outputs directly into static, fast-loading JavaScript data without heavy backend servers.
Complements historical machine learning forecasts with up-to-the-minute real-time price tracking for a comprehensive market analysis.
PREDICTION
Select your parameters to load pre-calculated price projections based on historical data.
Configure the model inputs to generate your projection
MODEL EVALUATION
Evaluating the LSTM architecture's performance on unseen test data spanning 602 days (Aug 2024 → Mar 2026). The model successfully maintains an error rate well below the 5% threshold.