To view market insights for cryptocurrency predictions:
python cli.py insightsThis will:
- Show you a list of available cryptocurrencies (ETH, BTC, SOL, BNB, ARB)
- Let you select a cryptocurrency
- Show available timeframes for that cryptocurrency
- Display detailed market analysis including:
- Price distribution analysis
- Statistical price ranges
- Network contributor information
- Confidence intervals
To get AI-powered liquidity management recommendations:
python liquidity_manager.py --asset [ETH, BTC, SOL, BNB, ARB] --amount [amount in chosen currency]This will:
- Analyze historical data for the selected asset
- Generate optimal liquidity allocation strategies
- Provide detailed market analysis including:
- Risk assessment
- Position sizing recommendations
- Market condition analysis
- Allocation breakdowns
Create a .env file in the project root with the following configurations
# Required for basic functionality
OPENAI_API_KEY=YOUR_OPENAI_API_KEY
Here's a detailed README focused on the liquidity and fee management components:
This system provides AI-powered liquidity management and fee analysis for cryptocurrency trading, leveraging the Allora Network's predictive capabilities. It combines real-time market data, funding rates across multiple exchanges, and machine learning predictions to optimize liquidity allocation and fee strategies.
The Liquidity Manager (liquidity_manager.py) provides sophisticated liquidity allocation strategies using historical data analysis and AI-powered predictions.
- Historical metrics analysis
- AI-powered strategy generation using GPT-4
- Risk assessment across multiple protocols
- Dynamic allocation recommendations
- Market condition analysis
def get_historical_metrics(self, asset: str) -> Dict:
"""Get historical price data and metrics for analysis"""
# Get 1 year of daily data
symbol = f"{asset}-USD"
data = yf.download(symbol, period="1y", interval="1d")
# Calculate basic metrics
metrics = {
"volatility": float(data['Close'].pct_change().std() * np.sqrt(252)),
"max_drawdown": self._calculate_max_drawdown(data['Close']),
"daily_returns": data['Close'].pct_change().dropna().values.tolist()[-30:], # Last 30 days
"current_price": float(data['Close'].iloc[-1]),
"price_30d_ago": float(data['Close'].iloc[-31])
}
return metrics
The system uses a carefully crafted prompt to generate liquidity strategies:
# Use triple quotes with raw string to avoid f-string interpretation of JSON structure
prompt = f"""
You are a DeFi expert analyzing liquidity provision strategies. Given the following metrics for {asset}:
- 30-day price change: {((metrics['current_price'] / metrics['price_30d_ago']) - 1) * 100:.2f}%
- Annualized volatility: {metrics['volatility'] * 100:.2f}%
- Maximum drawdown: {metrics['max_drawdown'] * 100:.2f}%
For the following protocols:
1. Curve (known for stable pairs, low IL risk, conservative)
2. Uniswap (balanced risk, medium IL exposure)
3. Balancer (flexible weights, medium-high IL risk)
Please provide:
1. A risk assessment for each protocol given current market conditions
2. Recommended allocation percentages across these protocols for a balanced strategy
3. Brief explanation for each allocation
4. Specific recommendations for:
- Optimal pool selection within each protocol
- Target price ranges for concentrated liquidity (if applicable)
- Rebalancing frequency suggestions
Format your response as JSON with the following structure:
{{
"protocol_risks": {{
"curve": {{"risk_level": "", "risk_factors": []}},
"uniswap": {{"risk_level": "", "risk_factors": []}},
"balancer": {{"risk_level": "", "risk_factors": []}}
}},
"allocations": {{
"curve": {{
"percentage": 0,
"reasoning": "",
"pool_recommendations": "",
"rebalancing_frequency": ""
}},
"uniswap": {{
"percentage": 0,
"reasoning": "",
"pool_recommendations": "",
"price_range": "",
"rebalancing_frequency": ""
}},
"balancer": {{
"percentage": 0,
"reasoning": "",
"pool_recommendations": "",
"rebalancing_frequency": ""
}}
}},
"market_outlook": "",
"general_recommendations": []
}}
"""The Fee Manager (fee_manager.py) analyzes funding rates and fees across multiple exchanges to identify arbitrage opportunities and optimize trading costs.
EXCHANGE_APIS = {
"okx": {
"funding_rate": "https://www.okx.com/api/v5/public/funding-rate",
"ticker": "https://www.okx.com/api/v5/market/ticker"
},
"dydx": {
"funding_rate": "https://api.dydx.exchange/v3/markets",
"ticker": "https://api.dydx.exchange/v3/markets"
},
"vertex": {
"funding_rate": "https://prod.vertexprotocol.com/v1/query/funding_rates",
"ticker": "https://prod.vertexprotocol.com/v1/query/market_summary"
},
"bitget": {
"funding_rate": "https://api.bitget.com/api/mix/v1/market/current-fundRate",
"ticker": "https://api.bitget.com/api/mix/v1/market/ticker"
},
"coinex": {
"funding_rate": "https://api.coinex.com/v1/contract/funding_rate",
"ticker": "https://api.coinex.com/v1/market/ticker"
},
"bingx": {
"funding_rate": "https://open-api.bingx.com/openApi/swap/v2/quote/fundingRate",
"ticker": "https://open-api.bingx.com/openApi/swap/v2/quote/price"
},
"cryptocom": {
"funding_rate": "https://api.crypto.com/v2/public/get-funding-rate",
"ticker": "https://api.crypto.com/v2/public/get-ticker"
}
}- Real-time funding rate analysis
- Cross-exchange fee comparison
- Historical fee pattern analysis
- Arbitrage opportunity detection
- Risk-adjusted fee optimization
The system integrates with Allora Network's prediction API for enhanced market insights:
ALLORA_API_BASE = "https://allora-api.testnet.allora.network/emissions/v5"
TOPICS = {
"ETH": {
"price": 13, # ETH 5min Price Prediction
"volatility": 15 # ETH 5min Volatility Prediction
},
"BTC": {
"price": 14, # BTC 5min Price Prediction
"volatility": 16 # BTC 5min Volatility Prediction
}
}The system uses Allora's predictions for:
- Price movement forecasting
- Volatility predictions
- Market sentiment analysis
- Risk assessment
python liquidity_manager.py --asset [ETH/BTC] --amount [amount]This will:
- Analyze historical market data
- Generate AI-powered allocation strategies
- Provide protocol-specific recommendations
- Display risk assessments and confidence metrics
python fee_manager.py --asset [ETH/BTC]Outputs:
- Current funding rates across exchanges
- Fee arbitrage opportunities
- Historical fee patterns
- Risk-adjusted recommendations
-
Market Data Collection
- Historical price data from yfinance
- Real-time exchange data
- Allora Network predictions
-
Analysis Layer
- Volatility calculations
- Risk metrics computation
- Cross-exchange fee analysis
- Protocol-specific risk assessment
-
Strategy Generation
- GPT-4 analysis of market conditions
- Protocol-specific recommendations
- Risk-adjusted allocation strategies
- Fee optimization suggestions
The system provides rich visualization of analysis results:
def plot_market_analysis(self, data: Dict, confidence_intervals: List[float], percentiles: List[str]):
"""Create an enhanced market analysis visualization."""
fig = make_subplots(
rows=3, cols=1,
subplot_titles=(
"Price Distribution Analysis",
"Statistical Price Range (±3σ)",
"Top Market Contributors"
),
vertical_spacing=0.2,
row_heights=[0.4, 0.3, 0.3]
)
# 1. Improved Distribution Plot
fig.add_trace(
go.Violin(
y=confidence_intervals,
box_visible=True,
line_color='rgba(41, 128, 185, 0.8)',
fillcolor='rgba(52, 152, 219, 0.3)',
meanline_visible=True,
name="Distribution",
showlegend=False,
points=False, # Hide individual points
width=0.8, # Adjust width of violin plot
meanline=dict(
color="rgba(41, 128, 185, 1.0)",
width=2
),
box=dict(
line=dict(
color="rgba(41, 128, 185, 1.0)",
width=2
)
)
),
row=1, col=1
)
# Add cleaner percentile annotations on both sides
for i, (percentile, value) in enumerate(zip(percentiles, confidence_intervals)):
# Left side annotation
fig.add_annotation(
x=-0.3,
y=value,
text=f"${value:.2f}",
xref="x",
yref="y",
showarrow=True,
arrowhead=2,
arrowsize=1,
arrowwidth=2,
arrowcolor='rgba(41, 128, 185, 0.6)',
font=dict(size=10, color='rgba(41, 128, 185, 1.0)'),
align='right',
row=1, col=1
)
# Right side annotation
fig.add_annotation(
x=0.3,
y=value,
text=f"{percentile}",
xref="x",
yref="y",
showarrow=True,
arrowhead=2,
arrowsize=1,
arrowwidth=2,
arrowcolor='rgba(41, 128, 185, 0.6)',
font=dict(size=10, color='rgba(41, 128, 185, 1.0)'),
align='left',
row=1, col=1
)
# Update layout
fig.update_layout(
title={
'text': "Cryptocurrency Price Analysis Dashboard",
'y':0.98,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top',
'font': dict(size=24, color='rgba(41, 128, 185, 1.0)')
},
showlegend=False,
height=1200,
template="plotly_white",
margin=dict(t=100, b=50, l=120, r=120), # Increased margins for labels
paper_bgcolor='white',
plot_bgcolor='rgba(240, 247, 255, 0.5)'
)
# Update axes
fig.update_yaxes(
title_text="Price (USD)",
title_font=dict(size=12),
gridcolor='rgba(189, 195, 199, 0.4)',
row=1, col=1
)
fig.update_xaxes(
showticklabels=False, # Hide x-axis labels
showgrid=False, # Hide x-axis grid
zeroline=False, # Hide zero line
row=1, col=1
)
# 2. Improved Standard Deviation Plot
median = confidence_intervals[2]
std = (confidence_intervals[3] - confidence_intervals[1]) / 2
x_range = np.linspace(median - 3.5*std, median + 3.5*std, 100)
y_range = np.exp(-0.5 * ((x_range - median) / std) ** 2) / (std * np.sqrt(2 * np.pi))
fig.add_trace(
go.Scatter(
x=x_range,
y=y_range,
mode='lines',
line=dict(color='rgba(41, 128, 185, 0.8)', width=3),
fill='tonexty',
fillcolor='rgba(52, 152, 219, 0.1)',
name='Normal Distribution',
showlegend=False
),
row=2, col=1
)
# Add styled standard deviation markersRequired in .env:
OPENAI_API_KEY=your_api_key_here
The system supports multiple exchanges with configurable endpoints and parameters:
EXCHANGE_APIS = {
"okx": {
"funding_rate": "https://www.okx.com/api/v5/public/funding-rate",
"ticker": "https://www.okx.com/api/v5/market/ticker"
},
"dydx": {
"funding_rate": "https://api.dydx.exchange/v3/markets",
"ticker": "https://api.dydx.exchange/v3/markets"
},
"vertex": {
"funding_rate": "https://prod.vertexprotocol.com/v1/query/funding_rates",
"ticker": "https://prod.vertexprotocol.com/v1/query/market_summary"
},
"bitget": {
"funding_rate": "https://api.bitget.com/api/mix/v1/market/current-fundRate",
"ticker": "https://api.bitget.com/api/mix/v1/market/ticker"
},
"coinex": {
"funding_rate": "https://api.coinex.com/v1/contract/funding_rate",
"ticker": "https://api.coinex.com/v1/market/ticker"
},
"bingx": {
"funding_rate": "https://open-api.bingx.com/openApi/swap/v2/quote/fundingRate",
"ticker": "https://open-api.bingx.com/openApi/swap/v2/quote/price"
},
"cryptocom": {
"funding_rate": "https://api.crypto.com/v2/public/get-funding-rate",
"ticker": "https://api.crypto.com/v2/public/get-ticker"
}
}- Price distribution analysis
- Volatility modeling
- Funding rate analysis
- Protocol risk assessment
- Cross-exchange arbitrage detection
- GPT-4 for strategy generation
- Allora Network for price predictions
- Custom ML models for risk assessment
- Exchange APIs for real-time data
- Historical price data from yfinance
- Protocol-specific metrics
- Network consensus data from Allora
This system provides a comprehensive solution for cryptocurrency liquidity management and fee optimization, combining traditional market analysis with cutting-edge AI predictions and cross-exchange analysis.
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