✳️How It Works ?

Blocklens AI is designed to make futures trading smarter, more precise, and fully community-driven.

Here’s how the system works step by step:

Step 1 - AI Scans the Market

Blocklens AI is always on watch, scanning the entire futures market in real time, 24/7, to catch hidden opportunities before they emerge. Instead of chasing trades manually, the AI constantly evaluates every listed perpetual pair and filters out only the setups with strong potential.

Step 2 - Indicator Analysis

How Blocklens AI Uses Indicator Analysis ?

Indicators are at the core of Blocklens AI’s decision-making engine. Instead of relying on just one signal, the AI combines multiple indicators to create a confluence score that reflects the real market strength.

Step 1: Real-Time Indicator Calculation The AI continuously computes values for RSI, MACD, EMA crossovers, Volume shifts, and price action zones across all supported futures pairs.

Step 2: Detecting Market Conditions

• RSI → Identifies if the market is overbought or oversold.

• MACD → Shows momentum shifts and potential reversals.

• EMA Crossovers → Confirms short-term vs long-term trend direction.

• Volume Heatmap → Detects unusual buying/selling pressure.

• Price Action Patterns → Finds key formations that reveal hidden market psychology.

Step 3: Confluence Scoring The AI assigns points when conditions align (e.g., RSI oversold + MACD bullish + rising volume = strong long setup). If enough factors agree, the setup is marked as high probability.

Step 4: Signal Validation with AI Model Past data is used to train the AI, so it recognizes which indicator combinations historically led to profitable trades. Only when the probability passes a threshold does Blocklens AI push the signal.

Step 3 - Candlestick Patterns Analysis

How Blocklens AI Uses Candlestick Patterns

Blocklens AI doesn’t just spot candlestick shapes — it reads them in full market context to separate real signals from noise.

Step 1: Automatic Pattern Recognition The AI scans every candle across supported futures pairs and flags formations like Bullish Engulfing, Doji, Hammer, Morning Star, and more.

Step 2: Trend Validation Instead of treating patterns in isolation, Blocklens AI checks whether the broader market is in an uptrend, downtrend, or sideways range to confirm relevance.

Step 3: Indicator Confluence Each detected pattern is cross-verified with technical indicators such as RSI, MACD, EMA crossovers, and Volume strength. Only when patterns align with indicator signals do they qualify as potential trades.

Step 4 - AI Filtering

Raw signals are noisy. Blocklens AI refines every setup through a multi-layer validation system combining indicators, candlestick patterns, and machine learning.

Explanation

Raw signals are generated → We compute technical indicators (RSI, EMA, MACD, Volume) and detect candlestick patterns.

Step 1: Calculate indicators

• RSI, MACD, EMA crossovers, Volume changes and price action.

Step 2: Create an indicator confluence score

• If RSI oversold → +1

• If MACD bullish → +1

• If Volume rising → +1

• If EMA crossover confirmed → +1

Step 3: Candlestick pattern workflow

• Detect if the setup includes a Reversal (e.g., Bullish Engulfing, Hammer), Continuation (e.g., Rising Three Methods, Marubozu), or Indecision (e.g., Doji, Spinning Top).

• Match the pattern to current trend context (uptrend, downtrend, sideways).

• Assign a pattern score: Strong (+1), Neutral (0), Weak (ignore).

• Add candlestick score to the indicator confluence.

Step 4: Confluence check

• If 2 or more conditions (indicators + candlestick pattern) align → that’s a strong setup, like pro traders confirm before taking trades.

Step 5: Model training

• A RandomForestClassifier learns which indicator + candlestick combinations historically led to winning trades.

Here is an example code of how Blocklens AI uses a RandomForestClassifier to train on historical indicator and candlestick data. ⮯

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score

# Example historical dataset (indicators + candlestick patterns)
data = {
    "RSI": [25, 72, 40, 65, 29, 85, 33, 55],
    "MACD": [1, -1, 0, 1, -1, -1, 1, 0],       # 1 = bullish, -1 = bearish, 0 = neutral
    "EMA_Crossover": [1, -1, 0, 1, -1, -1, 1, 0],  # 1 = bullish, -1 = bearish
    "Volume_Score": [1, 0, 1, 1, 0, 0, 1, 1],   # 1 = rising, 0 = weak
    "Pattern_Score": [1, -1, 0, 1, -1, -1, 0, 1], # candlestick score
    "Profitable": [1, 0, 0, 1, 0, 0, 1, 1]     # Target: 1 = profitable, 0 = not
}

# Convert to DataFrame
df = pd.DataFrame(data)

# Features and target
X = df[["RSI", "MACD", "EMA_Crossover", "Volume_Score", "Pattern_Score"]]
y = df["Profitable"]

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train Random Forest Classifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluation
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Classification Report:\n", classification_report(y_test, y_pred))

# Example: Predict probability of a new setup
new_setup = [[28, 1, 1, 1, 1]]  # Oversold RSI, Bullish MACD, EMA up, Rising Volume, Bullish Pattern
probability = model.predict_proba(new_setup)[0][1]

print("Probability this setup is profitable:", round(probability * 100, 2), "%")

Step 5 - Smart Entry & Exit Levels

Blocklens AI signals are designed to be clear, simple, and executable. Each signal includes one entry zone, one stop-loss, and one take-profit, making it easy for traders to follow without over-complication.

🔹Entry Zone

Defined using liquidity zones, EMA clusters, and candlestick confirmations.

• Example: “Entry Zone: $4,800 – $4,810 (ETH/USDT)

🔹 Stop-Loss (SL)

Placed just beyond the invalidation level to protect capital.

• For longs → below support or candlestick low.

• For shorts → above resistance or candlestick high.

🔹 Take-Profit (TP)

Blocklens AI sets one precise TP level using Fibonacci extensions, ATR volatility, and historical reaction points. This gives traders a clear target for closing the trade.

• Example: TP = $4,850 (ETH/USDT)

Risk-to-Reward Check Every signal is validated by its Risk-to-Reward ratio.

• If R/R < 1.3 → filtered out.

• If R/R ≥ 1.4 → ✅ published.

Step 6 - Leverage Adjustment

Blocklens AI doesn’t just calculate signal confidence — it also adjusts the suggested leverage based on the strength of the setup. This way, stronger signals = more aggressive leverage, while weaker signals stick to safer modes.

Weighted Confidence System (recap)

• RSI Oversold/Overbought → +15%

• MACD Momentum Shift → +20%

• EMA Crossovers → +15%

• Volume Spike / Heatmap → +15%

• Candlestick Reversal → +25%

• Candlestick Continuation → +15%

• Doji (Indecision at top) = +10%

Confidence → Leverage Mapping

Blocklens AI maps final confidence % into risk-adjusted leverage tiers:

• 0–30% = Weak → Ignore ❌ No trade taken. (Avoids overtrading low-quality setups.)

• 30–60% = Medium → Safe Mode (1–5x) Low leverage to reduce risk. Example: RSI + EMA cross but weak candlestick.

• 60–80% = Strong → Balanced Mode (5–10x) Moderate leverage for strong setups. Example: RSI oversold + MACD bullish + Rising Volume.

• 80%+ = High Confidence → Aggressive Mode (10–20x) Full conviction signals where confluence is perfect. Example: Bullish Engulfing + Oversold RSI + MACD bullish + Volume spike.

Example Leverage Data