Getting Started

Getting Started with PyPScript

Your first trading strategy in 10 minutes

This guide will walk you through creating, understanding, and deploying your first PyPScript trading strategy. No prior programming experience required.

What You'll Build

A simple moving average crossover strategy that:

Buys when fast MA crosses above slow MA
Sells when fast MA crosses below slow MA
Uses RSI for confirmation

Prerequisites

Access to PyP Dashboard (pyp.stanlink.online)
Basic understanding of trading concepts (moving averages, RSI)
10 minutes of your time

Step 1: Create a New Project

Login to PyP Dashboard
- Navigate to pyp.stanlink.online
- Sign in with your account
Create New Project
- Click "New Project"
- Name: "My First Strategy"
- Description: "Learning PyPScript basics"
- Click "Create"
Open the Editor
- Click on your new project
- Navigate to "Editor" tab
- You'll see a blank .ppc file

Step 2: Write Your First Strategy

Copy and paste this code into the editor:

#!pyp/1.0
engine: pyp-1.0

// Import the indicators and functions we need
import indicators.sma
import indicators.rsi
import functions.cross
import functions.above
import functions.below

// Define our strategy
strategy "ma_crossover_rsi" {
    // Calculate moving averages
    fast_ma = sma.evaluate(close, 10)
    slow_ma = sma.evaluate(close, 20)
    
    // Calculate RSI for confirmation
    momentum = rsi.evaluate(close, 14)
    
    // Define our trading patterns
    patterns {
        // Buy when fast MA crosses above slow MA and RSI > 50
        cross(fast_ma, slow_ma) and above(momentum, 50) -> UP(confidence: 0.80)
        
        // Sell when fast MA crosses below slow MA and RSI < 50
        cross(slow_ma, fast_ma) and below(momentum, 50) -> DOWN(confidence: 0.80)
        
        // Hold in all other cases
        default -> HOLD(confidence: 0.95)
    }
}

// Required metadata
meta {
    author: "Your Name"
    version: "1.0"
    description: "Simple MA crossover with RSI confirmation"
    tags: ["moving-average", "rsi", "crossover", "beginner"]
}

// Optional configuration
config {
    timeframes: ["1h", "4h"]
    pairs: ["EURUSD", "GBPUSD", "BTCUSDT"]
    lookback_period: 100
    optimization: "accuracy"
}

Step 3: Understand the Code

Let's break down each section:

Header

#!pyp/1.0
engine: pyp-1.0

Specifies PyPScript version 1.0
Required at the top of every file

Imports

import indicators.sma
import indicators.rsi
import functions.cross

Import the technical indicators and functions you'll use
Only import what you need for better performance

Strategy Block

strategy "ma_crossover_rsi" {
    // Your strategy logic goes here
}

Contains all your trading logic
Strategy name must be unique

Indicator Calculations

fast_ma = sma.evaluate(close, 10)
slow_ma = sma.evaluate(close, 20)
momentum = rsi.evaluate(close, 14)

Calculate technical indicators
close is the closing price (built-in variable)
Numbers are the indicator parameters (periods)

Pattern Matching

patterns {
    cross(fast_ma, slow_ma) and above(momentum, 50) -> UP(confidence: 0.80)
    default -> HOLD(confidence: 0.95)
}

Define when to buy, sell, or hold
Each pattern has a confidence level (0.0 to 1.0)
default catches all other cases

Metadata

meta {
    author: "Your Name"
    version: "1.0"
    description: "Strategy description"
}

Required information about your strategy
Used in the marketplace and for tracking

Configuration

config {
    timeframes: ["1h", "4h"]
    pairs: ["EURUSD", "BTCUSDT"]
}

Optional settings for training and deployment
Specifies which markets and timeframes to use

Step 4: Validate Your Code

Save the File
- Press Ctrl+S (Windows) or Cmd+S (Mac)
- Or click the "Save" button
Validate Syntax
- Click "Validate" button
- Check the console panel for any errors
- Green checkmark means success!

Common Errors and Fixes

Error: Missing import for 'cross'
Fix: Add 'import functions.cross'

Error: Invalid confidence value
Fix: Use values between 0.0 and 1.0

Error: Missing default pattern
Fix: Add 'default -> HOLD(confidence: 0.95)'

Step 5: Train Your Strategy

Navigate to Training
- Click "Training" tab in your project
- Click "New Training Run"
Configure Training
- Pairs: Select "EURUSD" and "BTCUSDT"
- Timeframes: Select "1h"
- Date Range: Last 3 months
- Sample Size: 50,000 samples
Start Training
- Review your settings
- Click "Start Training"
- Watch the live progress

Training Process

[1/4] Loading market data... ✓
[2/4] Computing indicators... ✓
[3/4] Generating training samples... ✓
[4/4] Optimizing strategy parameters... ✓

Training complete! Accuracy: 67.3%

Step 6: Test in Paper Trading

Navigate to Simulation
- Click "Simulation" tab
- Click "New Simulation"
Configure Simulation
- Strategy Version: Select your trained version
- Pairs: EURUSD
- Timeframe: 1h
- Initial Balance: $10,000
- Risk per Trade: 2%
Run Simulation
- Click "Start Simulation"
- Watch live trades in the chart
- Monitor portfolio performance

Simulation Results

Portfolio Balance: $10,450
Total Trades: 23
Win Rate: 65.2%
Max Drawdown: -3.2%
Sharpe Ratio: 1.8

Step 7: Deploy for Live Signals

Navigate to Deployment
- Click "Deploy" tab
- Select your trained strategy
Choose Delivery Channels
- ✓ Telegram Bot
- ✓ Discord Webhook
- ✓ Email Notifications
- ✓ Dashboard Alerts
Deploy Strategy
- Click "Deploy to Production"
- Your strategy is now live!

Understanding the Results

What Just Happened?

You wrote declarative code - Described what you want, not how to do it
AI optimized parameters - Found the best indicator settings automatically
AI compilation - Your strategy became a highly optimized AI model
Fast prediction - Brain Worker uses the trained model for real-time predictions

Why This Works

Traditional Approach:
Code → Rules → If/Then Logic → Decision

PyPScript Approach:
Intent → AI Training → AI Model → Prediction Engine → Decision

The AI found patterns in historical data that match your intent, then encoded them into a model for ultra-fast prediction.

Next Steps

Improve Your Strategy

Add more indicators - Try MACD, Bollinger Bands, or Fractals
Multi-timeframe analysis - Combine 1h and 4h signals
Risk management - Add stop-loss and take-profit patterns

Learn Advanced Features

Pattern Matching Guide - Complex conditions
Strategy Validation - Validation methods
Running Training - Optimization techniques

Join the Community

Discord Server - Get help and share strategies
Telegram Group - Latest updates and announcements

Common Questions

Q: Do I need programming experience?

A: No! PyPScript is designed for traders, not programmers. If you understand moving averages and RSI, you can write strategies.

Q: How accurate are the predictions?

A: Accuracy varies by strategy and market conditions. Good strategies achieve 55-70% accuracy with proper risk management.

Q: Can I use custom indicators?

A: Currently, we support built-in indicators. Custom indicators are coming in future updates.

Q: Is my strategy code private?

A: Yes! Your strategies are private by default. You can choose to share them in the marketplace for monetization.

Q: What markets are supported?

A: Forex (all major pairs), Crypto (Bitcoin, Ethereum, major altcoins), with Commodities and Indices coming soon.

Troubleshooting

Strategy Won't Validate

Check all imports are correct
Ensure confidence values are between 0.0 and 1.0
Make sure you have a default pattern
Verify all parentheses are balanced

Training Fails

Reduce sample size if timeout occurs
Check date range has sufficient data
Ensure selected pairs have data for the timeframe
Try simpler strategies first

Poor Performance

Add more confirmation indicators
Increase confidence thresholds
Test on different timeframes
Consider market conditions during training period

Success Tips

Start Simple - Master basic patterns before adding complexity
Test Thoroughly - Always paper trade before going live
Understand Markets - PyPScript amplifies your trading knowledge
Iterate Quickly - Try many variations to find what works
Join Community - Learn from other successful strategy developers

Congratulations! You've created, trained, and deployed your first PyPScript strategy. You're now ready to explore more advanced features and build profitable trading systems.

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Last updated: February 2026

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