Use Case: Financial Markets - Technical Analysis

Overview

Real-time trading signal generation using classic technical indicators and SASE+ pattern detection.

Target Market

• Crypto trading (BTC, ETH, altcoins)
• Forex (currency pairs)
• Equities (stocks, ETFs)
• Commodities (gold, oil, etc.)

Technical Indicators Implemented

Trend Indicators

Indicator	Description	Signal
SMA (Simple Moving Average)	Average price over N periods	Trend direction
EMA (Exponential Moving Average)	Weighted average favoring recent prices	Faster trend detection
Golden Cross	SMA20 crosses above SMA50	Bullish reversal
Death Cross	SMA20 crosses below SMA50	Bearish reversal

Momentum Indicators

Indicator	Description	Signal
RSI (Relative Strength Index)	Measures speed/change of price movements	Overbought (>70) / Oversold (<30)
MACD	Difference between EMA12 and EMA26	Trend momentum and crossovers
MACD Histogram	MACD - Signal line	Momentum strength

Volatility Indicators

Indicator	Description	Signal
Bollinger Bands	SMA ± 2 standard deviations	Volatility and breakouts
Bandwidth	Band width as % of middle band	Squeeze detection
%B	Price position within bands	Overbought/oversold within bands

Signal Types

Individual Signals

Confluence Signals

The most powerful signals come from indicator confluence - when multiple indicators align:

Strong Buy Conditions:

• RSI < 35 (oversold but not extreme)
• %B < 0.2 (price near lower Bollinger Band)
• MACD histogram > 0 (positive momentum)
• SMA20 > SMA50 (uptrend)

Strong Sell Conditions:

• RSI > 65 (overbought but not extreme)
• %B > 0.8 (price near upper Bollinger Band)
• MACD histogram < 0 (negative momentum)
• SMA20 < SMA50 (downtrend)

Architecture

Example Output

{
  "signal_type": "STRONG_BUY",
  "direction": "BUY",
  "symbol": "BTC/USD",
  "price": 42150.00,
  "confidence": 0.85,
  "indicators": {
    "rsi": 32.5,
    "bb_percent_b": 0.15,
    "macd_histogram": 125.3,
    "trend": "bullish"
  },
  "reason": "Multiple bullish indicators aligned: RSI oversold, price near lower BB, positive MACD histogram, uptrend",
  "timestamp": "2026-01-23T01:15:00Z"
}

Comparison with Traditional Solutions

Aspect	Traditional (Python/Java)	Varpulis
Latency	10-100ms	< 1ms
Code complexity	500+ lines	~400 lines (declarative)
Multi-indicator join	Manual state management	Native stream joins
Pattern detection	Custom implementation	Built-in SASE+
Scaling	Manual threading	Declarative parallelization
Observability	Add-on libraries	Built-in metrics

Risk Disclaimer

⚠️ This example is for educational purposes only.

• Technical indicators are lagging and not predictive
• Past performance does not guarantee future results
• Always use proper risk management
• Never invest more than you can afford to lose
• This is not financial advice

Running the Example

# Parse and validate
varpulis check examples/financial_markets.vpl

# Run with event file simulation
varpulis simulate -p examples/financial_markets.vpl -e market_data.evt


# On a bus, the program is a Vejas detect unit: put it under detects/

See Also

• VPL Syntax
• HVAC Building Example