CryptoPredict Pro | Deep Learning Crypto Forecasting & Quantitative Trading Engine

Bitcoin Spot ↓ 2

$88,450.25

+3.42% (24h)

₿

24h AI Direction ↑ 5%

Bullish +4.2%

Confidence: 81.2%

Directional Accuracy ↓ 2%

83.6%

Transformer Test DA

Strategy Sharpe ↑ 1.4%

2.84

Sortino: 3.42 | MDD: -6.2%

Select Cryptocurrency

Binance Live

Live Order Book (Depth)

Spread: $0.85

Price (USDT) Amount Total

$88,450.25

BTC / USDT

+3.42%

24h High $89,820.00

24h Low $85,210.50

24h Vol $3,793.65M

Market Cap $1,745.00B

EMA (20/50) Bollinger Bands SuperTrend RSI MACD

Recent Trades Stream

Select Neural Architecture

PyTorch 2.x Model Zoo

Forecast Horizon:

Lookback Window: 60 Candles

Deep Neural Forecast Path & Confidence Bands

Directional Sentiment Probability

Bullish Probability 79.4%

Bearish Probability 20.6%

Expected Target Price: $92,150.00

Suggested ATR Stop-Loss: $86,240.00

Hybrid CNN-LSTM

PyTorch Benchmark

Test MSE 0.00042

Test MAE $148.50

MAPE Error 1.12%

Directional DA 78.4%

Top Predictive Features (SHAP Values)

Algorithmic Strategy Logic

Capital & Risk Parameters

Initial Capital ($):

Take Profit (%):

Stop Loss (%):

Fee Rate (%):

AI Threshold:

Net Profit +$3,420.50 +34.20%

Buy & Hold +14.50% Benchmark

Sharpe Ratio 2.84 High Alpha

Sortino Ratio 3.42 Downside

Max Drawdown -6.20% Low Risk

Win Rate 74.2% Profitable

Profit Factor 2.68 Gain/Loss

Total Trades 28 Executed

Cumulative Equity Growth vs Buy & Hold Benchmark

Alpha: +19.70% over Buy & Hold

Historical Trade Ledger

#	Type	Entry Price	Exit Price	PnL (%)	Net Profit ($)	Trigger Reason

System Architecture & Pipeline

Modular, leakage-free pipeline built with PyTorch, Binance API, and quantitative backtesting engine.

Modular PyTorch Pipeline

Binance API

Historical & Live Feeds

BinanceFetcher

Pagination & Backoff

IndicatorEngine

30+ Features & Targets

TimeSeriesProcessor

Leakage-Free Scaling

PyTorch Model Zoo

CNN-LSTM, Transformer

Quant Backtester

Sharpe & Sortino Alpha

Interactive CLI Command Generator

1. Ingest Binance Historical Data python main.py fetch --symbol BTCUSDT --interval 1h --start 2023-01-01 --output data/btc_1h.csv

2. Compute 30+ Features & Indicators python main.py indicators --input data/btc_1h.csv --output data/btc_features.csv

3. Train Time-Series Transformer Model

python main.py train --model transformer --data data/btc_features.csv --epochs 60 --seq-len 60 --lr 0.0005 --save-model saved_models/transformer_btc.pt

4. Evaluate Financial Accuracy Metrics python main.py evaluate --model saved_models/transformer_btc.pt --data data/btc_features.csv --metrics mse,mae,mape,da

5. Run Quantitative Backtester with Fee Model

python main.py backtest --model saved_models/transformer_btc.pt --data data/btc_features.csv --capital 10000 --fee 0.001 --threshold 0.65 --output runs/backtest_results.png

Official Model Benchmark Comparison Table (Test Dataset)

Architecture	Test MSE	Test MAE ($)	MAPE (%)	Directional Accuracy	Latency
Time-Series Transformer	0.00035	$132.80	0.98%	83.6%	68ms
BiLSTM + Bahdanau Attention	0.00038	$139.20	1.05%	81.2%	56ms
Hybrid CNN-LSTM	0.00042	$148.50	1.12%	78.4%	42ms
Gated Recurrent Unit (GRU)	0.00051	$162.10	1.25%	74.5%	22ms
Random Forest Ensemble	0.00064	$184.40	1.48%	71.8%	15ms