Python Trading Bot Skill

Generate, refactor, and maintain production-ready Python trading bots for NQ futures prop firm trading with:

•Databento API for real-time and historical market data
•TradersPost webhooks for order execution (Apex Trader Funding compatible)
•Eval and PA modes with trailing drawdown, profit targets, dynamic sizing
•5 backtested signals + support for custom strategies
•Production features (state persistence, news filtering, alerting, comprehensive testing)

Commands

Bot Generation

Command	Description
`/bot:new`	Start interactive bot builder (with eval/PA mode selection)
`/bot:new <signal>`	Start with signal pre-selected
`/bot:custom`	Build bot with custom strategy/indicators
`/bot:keltner`	Generate Keltner Channel bot (PF 10.04)
`/bot:ema`	Generate EMA Cross bot (PF 6.23)
`/bot:vwap`	Generate Rolling VWAP bot (PF 5.20)
`/bot:supertrend`	Generate SuperTrend bot (PF 4.41)
`/bot:alligator`	Generate Alligator bot (PF 4.16)
`/bot:list`	Show signals with backtest metrics

Bot Refactoring

Command	Description
`/bot:refactor <bot_name>`	Start interactive refactoring session
`/bot:add-feature <bot_name> <feature>`	Add feature (news filter, indicator, etc.)
`/bot:remove-feature <bot_name> <feature>`	Remove feature cleanly
`/bot:migrate-pa <bot_name>`	Convert eval bot to PA mode
`/bot:analyze <bot_name>`	Analyze code quality and suggest improvements

Top 5 Entry Signals

Signal	Profit Factor	Win Rate	Key Parameters
keltner	10.04	87.8%	EMA=20, ATR_MULT=2.75
ema_cross	6.23	82.7%	Fast=3, Slow=8, Sep=0.35
vwap	5.20	85.3%	Window=720, Mult=1.0
supertrend	4.41	82.5%	Period=10, Mult=3.0
alligator	4.16	83.5%	Jaw=13, Teeth=8, Lips=5

Metrics from 219-day NQ futures backtest (May 2025 - Jan 2026) with ADX di_rising, threshold 35.

When to Use This Skill

Use this skill when:

•Creating a new live trading bot for NQ futures
•Refactoring or modifying existing trading bots
•Converting eval bots to PA mode (or vice versa)
•Adding features to bots (news filter, alerts, new indicators)
•Building custom strategies beyond the 5 pre-built signals
•Setting up Apex Trader Funding eval or PA accounts
•Implementing prop firm risk management (trailing DD, dynamic sizing)
•Testing strategies with CSV backtest before going live

Do NOT use for:

•Offline backtesting only (use Python/backtest.py instead)
•Creating platform indicators (use indicator-generator skill for TradingView/NinjaTrader/Tradovate)
•Multi-account management (run separate bot instances)
•Non-futures markets (skill optimized for NQ futures)

Generated Bot Structure

Each generated bot includes these files:

code

Python/bots/<signal>/
├── bot_<signal>.py              # Main entry point (asyncio)
├── config.py                    # Configuration from .env
├── databento_client.py          # Market data (Databento API)
├── traderspost_client.py        # Order execution (TradersPost webhook)
├── risk_manager.py              # Apex account tracking (eval/PA modes)
├── indicators.py                # Self-contained indicator calculations
├── news_calendar.py             # Economic calendar filtering (optional)
├── alerting.py                  # Email/SMS alerts (optional)
├── bot_state.db                 # SQLite persistence (created at runtime)
├── requirements.txt             # Python dependencies
├── .env.example                 # Credential template
├── .gitignore                   # Exclude .env, logs, *.db
└── README.md                    # Bot documentation

Bot Architecture

code

┌─────────────────────────────────────────────────────────────┐
│                    DATABENTO API                             │
│  https://databento.com (real-time & historical 2-min bars)  │
│  + CSV files for backtesting                                 │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                      PYTHON BOT                              │
│  • Indicator calculation (EMA, ATR, ADX, signal-specific)   │
│  • Signal detection (edge-triggered crossovers)              │
│  • Apex account tracking (eval/PA modes, trailing DD)       │
│  • Position management (SL/TP/trailing stops)               │
│  • Re-entry logic (3 bars wait, ADX > 40)                   │
│  • News calendar filtering (blackout periods)               │
│  • State persistence (SQLite - survive restarts)            │
│  • Alerting (email/SMS on critical events)                  │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                   TRADERSPOST WEBHOOK                        │
│  POST https://webhooks.traderspost.io/trading/webhook/...   │
│  {ticker, action, quantity, stopLoss, takeProfit}           │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│          BROKER (Tradovate / Apex Trader Funding)           │
│  Order execution, position management, OCO brackets          │
└─────────────────────────────────────────────────────────────┘

Key Features

Feature	Implementation
Market Data	Databento API (real-time + historical) + CSV files
Account Modes	Eval (profit target, trailing DD) or PA (risk %, static DD)
Position Sizing	Fixed contracts (eval) or risk-based % (PA mode)
Indicator calculations	Self-contained (EMA, ATR, ADX, VWAP, signal-specific)
Signal detection	Edge-triggered crossovers (5 pre-built + custom)
Position management	Entry price, SL, TP, HWM/LWM, trailing stops
Re-entry logic	3 bars wait, ADX > 40, max 10 per direction
News calendar	Economic event blackouts (FOMC, NFP, CPI)
Alerting	Email + SMS on daily loss limit, DD breach, connection loss
State persistence	SQLite database (survive mid-day restarts)
Session filtering	Configurable allowed hours (ET timezone)
Volume filter	Volume > EMA(volume, 20)
Graceful shutdown	SIGINT/SIGTERM handlers, exit positions on shutdown
Logging	File + console with timestamps
Testing	CSV backtest mode, unit tests, integration tests

Quick Start

1. Interactive Mode

code

User: Create a live trading bot
Assistant: [Runs /bot:new workflow]
  - Step 1: Select signal (keltner, ema_cross, vwap, supertrend, alligator)
  - Step 2: Configure signal parameters
  - Step 3: Set risk parameters (contracts, daily max loss)
  - Step 4: Configure exits (SL, TP, trailing)
  - Step 5: API credentials guidance
  - Step 6: Generate bot files

2. Direct Signal

code

User: /bot:keltner
Assistant: [Generates Keltner bot with optimal settings]

3. Quick Mode

code

User: /bot:new keltner --quick
Assistant: [Uses all defaults, generates immediately]

ADX Filter Modes

All 5 modes from backtest.py supported:

Mode	Logic	Recommended
`traditional`	ADX > threshold
`di_aligned`	+ DIs aligned with direction
`di_rising`	+ Dominant DI rising	✅
`adx_rising`	+ ADX itself rising
`combined`	All conditions

Exit Priority Order

•Trailing Stop (if active AND hit)
•Stop Loss (hard protective stop)
•Take Profit (profit target)

Skill References

Core Documentation

Resource	Description
Interactive Workflow	Interactive bot generation workflow (includes account_mode)
Refactoring Guide	Bot refactoring and modification workflows
Configuration Index	Configuration index with links to topics
Signals Overview	Signal overview and selection guide
Apex Rules	Comprehensive Apex Trader Funding rules
Apex Compliance	Apex compliance guidelines for automated trading

Configuration Topics

Resource	Description
Apex Accounts	Account modes, presets, position sizing
Signal Parameters	All 5 signal parameters
ADX Modes	ADX filter modes with examples
Exits & Re-entry	Stop loss, take profit, trailing, re-entry
Risk & Filters	Risk mgmt, filters, alerts, persistence

Guides

Resource	Description
Eval vs PA Comparison	Eval vs PA mode comparison
Eval Mode Guide	Eval account detailed guide
PA Mode Guide	PA account detailed guide
Data Provider Guide	Data provider overview
Custom Provider Tutorial	Custom provider implementation
Provider Reference	Bar format, testing, best practices
Architecture	Architecture overview
Architecture Components	Component details
Data Flow	Bar processing, entry/exit flows
Deployment Guide	Deployment overview
VPS Setup	Server setup and installation
Systemd Service	Service configuration
Production Operations	Monitoring, security, backup
Economic Calendar	News calendar integration
Testing Guide	Unit, integration, CSV backtest testing
Setup Guide	Bot setup and deployment

Signal Details

Resource	Description
Top 5 Signals	Detailed top 5 with all parameters
Keltner Channel	Keltner Channel details
EMA Crossover	EMA Crossover details
Rolling VWAP	Rolling VWAP details
SuperTrend	SuperTrend details
Williams Alligator	Williams Alligator details

Templates

Resource	Description
Base Bot	Main bot template (with persistence, news, alerts)
Config	Config file template
Indicators	Indicators class overview
Core Indicators	EMA, SMA, SMMA, ATR, ADX
Signal Indicators	VWAP, Keltner, SuperTrend, Alligator
Apex Integration	Apex account integration template
Risk Manager	Risk manager with eval/PA support
Persistence	SQLite state persistence
News Filter	Economic calendar filtering
Alerting	Email/SMS alerting system
Data Provider	Data provider adapter interface
Test Suite	Test suite template
TradersPost Client	TradersPost webhook client
Tradovate Client	Tradovate WebSocket client

Workflows

Resource	Description
Add Feature	Add feature to existing bot
Remove Feature	Remove feature from bot
Change Indicator	Swap or modify indicators
Migrate to PA	Convert eval bot to PA mode
Custom Strategy	Build custom strategy bot

Examples

Resource	Description
Keltner Bot Example	Full working Keltner Channel bot example
Custom Data Provider	Example custom data provider adapter
Alert Config	Alerting configuration example
Indicator Tests	Unit test examples
Environment Template	.env file template with all required credentials

Related Files

File	Purpose
`Python/backtest.py`	Reference implementation (indicator calculations, signal logic)
`Python/docs/TRADOVATE_API_GUIDE.md`	TradersPost payload format, Tradovate WebSocket
`Python/STRATEGY.md`	Strategy specification