ALT Risk Strategy with Fear & Greed + ISM PMI📊 Overview
This advanced crypto trading strategy combines multiple macro indicators to identify optimal buy and sell zones for altcoins. It tracks the relationship between altcoin performance versus Bitcoin (ALT/BTC pairs) while incorporating broader market sentiment and economic data to generate risk-adjusted entry and exit signals.
🎯 Core Methodology
Base Risk Metric (65% weight):
MACD Momentum (5%): Normalized trend strength on weekly ALT/BTC pair
RSI (60%): Relative strength indicating overbought/oversold conditions
Price Deviation (35%): Distance from 150-period moving average
Fear & Greed Index (20% weight):
Analyzes market sentiment using multiple factors:
Price momentum and rate of return
Money flow and volume analysis
Volatility metrics (crypto: BVOL24H, traditional: VIX)
Dominance indicators (crypto: BTC.D, traditional: Gold)
Two modes: Crypto-focused or Traditional markets
Customizable smoothing and weighting
US ISM PMI Integration (15% weight):
Manufacturing economic indicator (contraction vs expansion)
PMI < 50 = Economic weakness = Better crypto buying opportunities
PMI > 50 = Economic strength = Risk-on environment
Configurable offset to lead/lag the signal
Daily data smoothed over customizable period
💰 Trading Logic
Tiered Buy System:
Level 1 (Risk < 70): Initial entry with conservative amount
Level 2 (Risk < 50): Double down as risk decreases
Level 3 (Risk < 30): Maximum accumulation at extreme lows
All purchases customizable by dollar amount
Tiered Sell System:
Level 1 (Risk > 70): Take partial profits (default 25%)
Level 2 (Risk > 85): Continue scaling out (default 35%)
Level 3 (Risk > 100): Final exit (default 40%)
Sells reset when new buys occur (can re-accumulate)
⚙️ Key Features
Multi-Asset Support: ETH, SOL, ADA, LINK, UNI, XRP, DOGE, AVAX, MATIC, RENDER, or custom
Exchange Selection: Works with Binance, Coinbase, Kraken, Bitfinex, Bybit
3Commas Integration: Optional webhook alerts for automated bot trading
Visual Risk Zones: Color-coded indicator (green/lime/yellow/orange/red/maroon)
Real-time Info Table: Displays current risk metric, F&G index, PMI value, weights, and position status
Flexible Weighting: Adjust influence of each component (Base/F&G/PMI)
Weekly Timeframe: Reduces noise and focuses on macro trends
📈 Use Cases
DCA Strategy: Dollar-cost averaging with intelligent timing
Swing Trading: Catching major market cycles (weeks to months)
Risk Management: Exit before major downturns, enter during fear
Macro Trading: Align crypto positions with economic conditions
Bot Automation: Connect to 3Commas for hands-free execution
🎓 Credits & Attribution
Original Concept & Base Risk Metric:
Inspired by community-developed ALT/BTC risk oscillators
Fear & Greed methodology adapted from crypto market sentiment research
Enhancements & Integration:
ISM PMI integration and weighting system
Multi-indicator combination framework
Tiered buy/sell logic with reset mechanism
3Commas webhook integration
Development:
Primary Development: Claude AI (Anthropic)
Collaboration & Testing: User feedback and iteration
Pine Script Implementation: TradingView v5
⚠️ Disclaimer
This strategy is for educational and informational purposes only. Past performance does not guarantee future results. Cryptocurrency trading involves substantial risk of loss. Always conduct your own research and consider your risk tolerance before trading. The strategy uses lagging indicators (weekly timeframe) which may not react quickly to sudden market changes.
🔧 Recommended Settings
For better performance than default conservative settings:
Increase buy amounts: Try $50/$75/$100 for more meaningful positions
Adjust thresholds: Consider 40/60/80 for more frequent entries
Test different weights: Experiment with F&G and PMI influence
Optimize for your asset: Different cryptos may require different parameters
Version: 1.0
Last Updated: December 2025
Compatible With: TradingView Pine Script v5
ISM
Macroeconomic Artificial Neural Networks
This script was created by training 20 selected macroeconomic data to construct artificial neural networks on the S&P 500 index.
No technical analysis data were used.
The average error rate is 0.01.
In this respect, there is a strong relationship between the index and macroeconomic data.
Although it affects the whole world,I personally recommend using it under the following conditions: S&P 500 and related ETFs in 1W time-frame (TF = 1W SPX500USD, SP1!, SPY, SPX etc. )
Macroeconomic Parameters
Effective Federal Funds Rate (FEDFUNDS)
Initial Claims (ICSA)
Civilian Unemployment Rate (UNRATE)
10 Year Treasury Constant Maturity Rate (DGS10)
Gross Domestic Product , 1 Decimal (GDP)
Trade Weighted US Dollar Index : Major Currencies (DTWEXM)
Consumer Price Index For All Urban Consumers (CPIAUCSL)
M1 Money Stock (M1)
M2 Money Stock (M2)
2 - Year Treasury Constant Maturity Rate (DGS2)
30 Year Treasury Constant Maturity Rate (DGS30)
Industrial Production Index (INDPRO)
5-Year Treasury Constant Maturity Rate (FRED : DGS5)
Light Weight Vehicle Sales: Autos and Light Trucks (ALTSALES)
Civilian Employment Population Ratio (EMRATIO)
Capacity Utilization (TOTAL INDUSTRY) (TCU)
Average (Mean) Duration Of Unemployment (UEMPMEAN)
Manufacturing Employment Index (MAN_EMPL)
Manufacturers' New Orders (NEWORDER)
ISM Manufacturing Index (MAN : PMI)
Artificial Neural Network (ANN) Training Details :
Learning cycles: 16231
AutoSave cycles: 100
Grid
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 998
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Network
Input nodes connected: 19
Hidden layer 1 nodes: 2
Hidden layer 2 nodes: 0
Hidden layer 3 nodes: 0
Output nodes: 1
Controls
Learning rate: 0.1000
Momentum: 0.8000 (Optimized)
Target error: 0.0100
Training error: 0.010000
NOTE : Alerts added . The red histogram represents the bear market and the green histogram represents the bull market.
Bars subject to region changes are shown as background colors. (Teal = Bull , Maroon = Bear Market )
I hope it will be useful in your studies and analysis, regards.
