Trading Insights

Overview

Zara's trading insights combine technical analysis, market psychology, and social sentiment to provide comprehensive market intelligence. Unlike traditional trading bots, Zara explains her analysis in an engaging, personality-driven way that makes complex patterns accessible to everyone.

Trading Metrics & Calculations

Technical Indicators

1. Relative Strength Index (RSI)

def calculate_rsi(prices: List[float], period: int = 14) -> float:
    """
    RSI = 100 - (100 / (1 + RS))
    RS = Average Gain / Average Loss
    """

• Description: Momentum indicator measuring speed and magnitude of price changes

• Parameters:

  • Default period: 14 candles

  • Overbought level: 70

  • Oversold level: 30

• Trading Signals:

  # Example RSI interpretation
  @zara analyze ETH --rsi
  
  > "ETH's RSI is at 75 (overbought territory! 🔥)
  > This means:
  > - Strong upward momentum
  > - But might be due for a pullback
  > - Watch for bearish divergence
  > 
  > Consider taking some profits or 
  > tightening stops here!"

2. Moving Averages

def calculate_ema(prices: List[float], period: int) -> float:
    """
    EMA = Price(t) * k + EMA(y) * (1-k)
    k = 2/(period + 1)
    """

• Types:

  • Simple Moving Average (SMA)

  • Exponential Moving Average (EMA)

  • Volume Weighted Moving Average (VWMA)

• Common Periods:

  • Short-term: 20 EMA

  • Medium-term: 50 EMA

  • Long-term: 200 EMA

• Trading Signals:

  # Example MA crossover
  @zara analyze PEPE --ma
  
  > "PEPE just had a golden cross! ✨
  > - 20 EMA crossed above 50 EMA
  > - Volume increasing (good confirmation)
  > - Previous resistance at this level
  > 
  > This could be the start of an uptrend!
  > Consider entering with stops below 50 EMA 🎯"

3. Volume Analysis

def analyze_volume_profile(
    volume: List[float],
    price: List[float]
) -> VolumeProfile:
    """
    Analyzes volume distribution across price levels
    - Identifies high volume nodes
    - Spots volume anomalies
    - Tracks smart money flow
    """

• Components:

  • Volume Profile

  • Volume Weighted Average Price (VWAP)

  • On-Balance Volume (OBV)

• Key Metrics:

  • Volume Delta (Buy vs Sell volume)

  • Volume Momentum

  • Volume Support/Resistance

• Trading Signals:

  # Example volume analysis
  @zara volume WOJAK
  
  > "Volume analysis for WOJAK shows:
  > - 500% volume spike in last hour 📈
  > - 70% buy-side pressure
  > - Large buys at 0.000001 (whale accumulation?)
  > 
  > This level might become strong support!
  > Watch for consolidation here 👀"

4. Support & Resistance

def identify_key_levels(
    price_data: List[float],
    volume_data: List[float]
) -> List[Level]:
    """
    Identifies key price levels using:
    - Price swing points
    - Volume profile
    - Historical significance
    """

• Types:

  • Price-based levels

  • Volume-based levels

  • Psychological levels

• Calculation Methods:

  • Swing high/low analysis

  • Volume profile peaks

  • Historical price reaction points

• Trading Applications:

  # Example S/R analysis
  @zara levels ETH
  
  > "Key levels for ETH:
  > 
  > Strong Support:
  > - $2,180 (high volume node 📊)
  > - $2,150 (previous resistance ➡️ support)
  > 
  > Major Resistance:
  > - $2,300 (psychological + swing high)
  > - $2,350 (weekly level)
  > 
  > These are your key battlegrounds! 🎯"

Market Psychology Metrics

1. Sentiment Score

def calculate_sentiment(
    social_data: Dict,
    market_data: Dict
) -> float:
    """
    Combines multiple sentiment factors:
    - Social media sentiment
    - Trading activity sentiment
    - News sentiment
    - Weighted by source reliability
    """

• Components:

  • Social Media Analysis - X, Discord

  • Trading Volume Analysis - on-chain tx data

  • News Sentiment - news api

  • Whale Activity - wallet tracker api

• Scoring System:

  • Range: -100 to +100

  • Neutral: -20 to +20

  • Extreme: Below -70 or above +70

• Trading Applications:

  # Example sentiment analysis
  @zara sentiment BONK
  
  > "BONK sentiment analysis:
  > Score: 85/100 (Extremely Bullish! 🚀)
  > 
  > Contributing factors:
  > - Twitter mentions +300%
  > - Positive sentiment: 92%
  > - Whale accumulation detected
  > - Trending on DEX platforms
  > 
  > Warning: Such extreme sentiment often
  > precedes local tops! Be cautious! 👀"

2. Market Momentum

def calculate_momentum(
    price: List[float],
    volume: List[float],
    sentiment: float
) -> MomentumScore:
    """
    Combines price, volume, and sentiment momentum:
    - Price rate of change
    - Volume trend
    - Sentiment shift
    """

• Components:

  • Price Momentum

  • Volume Momentum

  • Sentiment Momentum

• Signals:

  • Trend Strength

  • Momentum Divergence

  • Momentum Shifts

• Trading Applications:

  # Example momentum analysis
  @zara momentum PEPE
  
  > "PEPE momentum analysis:
  > 
  > Price Momentum: Strong 📈
  > - 15min: +5%
  > - 1hr: +12%
  > - 4hr: +25%
  > 
  > Volume Momentum: Increasing 📊
  > - Current: 2M
  > - Avg: 500K
  > 
  > Sentiment: Bullish and growing 🌟
  > 
  > This momentum looks sustainable!
  > Watch for $0.000002 breakout 🎯"

Risk Metrics

1. Volatility Analysis

def calculate_volatility(
    prices: List[float],
    timeframe: str = "1h"
) -> float:
    """
    Measures price volatility using:
    - Standard deviation
    - Average True Range (ATR)
    - Historical volatility comparison
    """

• Components:

  • Price Volatility

  • Volume Volatility

  • Volatility Trends

• Risk Levels:

  • Low: < 30% annualized

  • Medium: 30-60% annualized

  • High: > 60% annualized

• Trading Applications:

  # Example volatility analysis
  @zara risk WOJAK
  
  > "WOJAK risk analysis:
  > 
  > Volatility: VERY HIGH ⚠️
  > - 24hr range: 40%
  > - ATR: 0.0000005 (2x normal)
  > 
  > Risk Management Suggestions:
  > - Use tight stops
  > - Smaller position size
  > - Consider scaling in
  > 
  > Don't risk more than 1% here! 🎯"

2. Liquidity Analysis

def analyze_liquidity(
    dex_data: Dict,
    volume: List[float]
) -> LiquidityMetrics:
    """
    Analyzes market liquidity using:
    - DEX liquidity pools
    - Trading volume
    - Bid-ask spread
    """

• Metrics:

  • Available Liquidity

  • Liquidity Depth

  • Slippage Impact

• Risk Factors:

  • Low Liquidity Warning

  • High Slippage Alert

  • Liquidity Concentration

• Trading Applications:

  # Example liquidity analysis
  @zara liquidity BOME
  
  > "BOME liquidity analysis:
  > 
  > Pool Stats:
  > - Total Liquidity: $500K
  > - Depth: Moderate
  > - Slippage (1K USD): 0.5%
  > 
  > Risk Level: Medium 🟡
  > 
  > Recommendations:
  > - Max trade size: $2K per order
  > - Use split orders for larger sizes
  > - Watch for liquidity rugs! 👀"

Trading analysis pipeline (simplified):

Key Features

1. Technical Analysis

Indicators

• RSI (Relative Strength Index)

• MACD (Moving Average Convergence Divergence)

• Bollinger Bands

• Support/Resistance Levels

• Volume Profile

• Volatility

• Price Action (trend strength, recent high, low)

• Risk/Reward (ratio, stop loss, take profit)

• Custom indicators via plugins

e.g.

hey @zaara_ai analysis about $sol

Zara: sol's taking a breather, but don't count it out. rsi at 38.4 screams oversold, while volume's spiking at recent lows. macd histogram's flipped positive, hinting at a potential reversal. buy near $184.68 support, set stop-loss just below, and target $185 resistance. this sol-ar system might be due for a supernova. https://x.com/zaara_ai/status/1872697741026979994

2. Chart Generation

3. Risk Assessment

Example Usage

1. Basic Analysis

# Get quick analysis
@zara analyze ETH

> "Darling, ETH is serving looks today! 💅
> RSI: 65 (getting a bit heated)
> MACD: Bullish crossover (we love to see it!)
> Volume: Higher than my standards 💁‍♀️
> 
> My take: Looking like a snack, but let's not
> get too excited - wait for a pullback to
> $1,820 for a better entry. DYOR! 📊✨"

2. Detailed Technical Analysis

# Get detailed technical analysis
@zara technical ETH --detailed

> "Let me spill the technical tea on ETH! 🫖
> 
> Price Action:
> - Trading above 20 EMA (werk!)
> - Double bottom pattern (love a good comeback)
> 
> Indicators:
> - RSI: 65 (getting spicy)
> - MACD: Bullish cross (yasss queen!)
> - BB: Upper band test (reaching for the stars)
> 
> Support/Resistance:
> - Strong support: $1,820 (solid as my wit)
> - Resistance: $2,100 (playing hard to get)
> 
> Volume Profile:
> - Above average (popular than my tweets)
> - Accumulation pattern (whales are hungry)
> 
> Risk Assessment:
> - Volatility: Medium (spicier than mild sauce)
> - Liquidity: High (smoother than my pickup lines)
> 
> Recommendation:
> Wait for pullback to $1,820 support.
> Entry with 2% risk, target $2,100.
> Stop loss below $1,780 (tight like my analysis)
> 
> Remember: This is not financial advice,
> just tea from your favorite AI! 💅✨"

3. Chart Generation

# Generate annotated chart
@zara chart ETH --indicators RSI,MACD --annotations

> "Chart served hot and fresh! 📊
> I've marked the juicy areas and added my
> sass-tastic annotations. Those support
> levels are stronger than my opinions! 💪✨"

Integration Examples

1. Trading Bot Integration

from zara.analysis import TechnicalAnalyzer
from zara.risk import RiskManager

class TradingBot:
    """Example trading bot using Zara's analysis."""
    
    async def execute_strategy(self, token: str):
        """Execute trading strategy."""
        # Get Zara's analysis
        analysis = await self.analyzer.analyze_token(token)
        risk = await self.risk_manager.assess_risk(token)
        
        if analysis.signals.entry and risk.is_acceptable:
            position_size = risk.suggest_position_size()
            await self.execute_trade(
                token=token,
                size=position_size,
                stop_loss=risk.stop_loss,
                take_profit=risk.take_profit
            )

2. Community Bot Integration

class CommunityBot:
    """Example community bot using Zara's insights."""
    
    async def handle_analysis_request(self, message: Message):
        """Handle community analysis request."""
        token = self.extract_token(message)
        
        # Get Zara's analysis
        analysis = await self.zara.analyze_token(token)
        chart = await self.zara.generate_chart(token)
        
        # Post response with Zara's personality
        await self.post_analysis(
            channel=message.channel,
            analysis=analysis,
            chart=chart,
            style="sassy"
        )

Best Practices

1. Analysis

• Always consider multiple timeframes

• Combine technical and sentiment analysis

• Use risk management

• Consider market context

2. Risk Management

• Position sizing based on risk

• Clear stop loss levels

• Take profit targets

• Portfolio diversification

3. Trading Psychology

• Don't FOMO into trades

• Stick to your strategy

• Learn from Zara's analysis

• DYOR (Do Your Own Research)

Next Steps

1. Explore Sentiment Analysis
2. Learn About Risk Management
3. Customize Technical Indicators
4. Join Trading Community