$STG /USDT perpetual trading snapshot. Based on the data, here’s a quick breakdown.
Market Snapshot Summary:
Current Price: $0.2148
Mark Price: $0.2148
24h High/Low: $0.2182 / $0.2031
24h Volume:
STG: 23.00M
USDT: 4.87M
Recent Technical Indicators:
MA60: 0.2151
MA(5): 1,307
MA(10): 2,446
Recent Vol: 60
You could build a classifier model to predict whether to go LONG or SHORT on the next candle (15m or 1h):
1. Input Features:
Include:
OHLC data for last 15-60 candles
Moving Averages: MA(5), MA(10), MA(60)
Price deviation from MA60: current_price - MA60
Volume + volume delta
RSI, MACD, Bollinger Band %B (optional for better signal separation)
2. Output Label:
Binary:
1 if next candle closes higher than current (long signal)
0 if lower (short signal)
Model Skeleton (LSTM):
model = Sequential([ LSTM(64, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])), Dropout(0.2), LSTM(32), Dense(1, activation='sigmoid') ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
Trading Strategy (Backtest):
If model predicts > 0.6 → go long
If < 0.4 → go short
Else → stay out
Use trailing stop, take profit, and risk management rules to simulate performance.
Would you like a full Python code sample that fetches real STG/USDT data and trains a basic model like this? Follow for more updates.