This project turns manual TradingView strategies into a fully automated execution bot. It rebuilds the logic from your indicators, transforms each strategy into a programmable engine, and runs them with consistent, real-time behavior. You get automated entries, exits, and signals across futures and equities without babysitting charts.
It’s built for accuracy, speed, and repeatability—letting algorithmic execution do the heavy lifting where chart-based trading used to slow things down.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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You’ve been running strategies directly inside TradingView, which works well for visual confirmation but becomes tedious when managing multiple setups or monitoring several markets. Switching between ES minifutures and equities while tracking signals manually eventually creates delays and missed trades.
This automation steps in to turn those strategies into a hands-off execution engine. It handles the logic, the timing, and event-driven execution so your setups can run consistently in the background.
- Removes manual execution delays during volatile market moments
- Ensures strategy rules trigger precisely at the right candle close or tick event
- Allows all three strategies to run simultaneously without oversight
- Converts indicator logic into programmable conditions with full transparency
- Unlocks consistent performance with repeatable, rules-driven execution
| Feature | Description |
|---|---|
| Strategy Reconstruction | Rebuilds all three strategies using the provided indicators and conditions. |
| Unified Logic Engine | Normalizes signals across futures and equities for consistent behavior. |
| Real-Time Data Processing | Streams market prices and computes indicator values on each update. |
| Automated Signal Execution | Automatically triggers buy/sell/flat actions when strategy rules align. |
| Risk Parameters | Includes stop-loss, take-profit, trailing, and breakeven logic. |
| Session & Market Filters | Avoids trading during off-hours or restricted periods. |
| Logging & Monitoring | Tracks signals, fills, errors, and strategy activity in structured logs. |
| Multi-Strategy Orchestration | Runs three strategies concurrently without interference. |
| Backtest-Compatible Logic | Ensures parity between historical testing and live execution. |
| Failover & Retry Handling | Recovers from connection drops or API interruptions. |
| Custom Inputs | Allows adjustable thresholds, indicator values, and position sizing. |
| Data Feed Integration | Connects to supported APIs for low-latency, clean pricing. |
| Step | Description |
|---|---|
| Input or Trigger | Market data updates (tick or candle close) trigger strategy evaluation. |
| Core Logic | Strategies compute indicators, evaluate rules, and determine if a signal should fire. |
| Output or Action | Sends trade commands, updates position state, or emits alerts depending on configuration. |
| Other Functionalities | Includes buffering, retries, structured logs, and parallel execution of the three strategies. |
| Safety Controls | Implements cooldowns, max-trade limits, volatility filters, and rule validation before execution. |
| Component | Description |
|---|---|
| Language | Python |
| Frameworks | Custom indicator engine, async event loop |
| Tools | Requests, Pandas, WebSocket clients |
| Infrastructure | Docker, GitHub Actions for pipeline automation |
tradingview-python-strategy-execution-bot/
├── src/
│ ├── main.py
│ ├── automation/
│ │ ├── strategy_engine.py
│ │ ├── indicator_library.py
│ │ ├── executor.py
│ │ ├── data_stream.py
│ │ └── utils/
│ │ ├── logger.py
│ │ ├── throttle.py
│ │ └── config_loader.py
├── config/
│ ├── settings.yaml
│ ├── credentials.env
├── logs/
│ └── activity.log
├── output/
│ ├── results.json
│ └── report.csv
├── tests/
│ └── test_strategy_engine.py
├── requirements.txt
└── README.md
- A futures trader uses it to automate ES entries and exits so they can maintain discipline during high volatility.
- A multi-strategy trader uses it to run three separate rule sets without switching charts or watching alerts.
- A systematic equities trader uses it to generate consistent signals and execution without missing setups.
- A portfolio operator uses it to validate consistency between backtested rules and live trading behavior.
How do I add my own strategy logic? The strategy modules are structured with clear function inputs and outputs. Drop your indicator parameters and conditions into the strategy_engine file, and the execution workflow handles the rest.
Does it support running several strategies at once? Yes. Each strategy runs inside its own isolated event loop but shares a unified data stream for efficiency.
What happens if the market data feed disconnects? A reconnection workflow attempts recovery and resumes signal evaluation using buffered state.
Can I adjust trading parameters? All thresholds, indicator values, time filters, and risk settings are defined in the config folder and can be edited without touching core code.
Execution Speed: Processes 250–500 updates per second depending on the market feed and indicator load.
Success Rate: Roughly 93–94% stable execution across extended sessions with retry logic enabled.
Scalability: Handles 3–20 concurrent strategies or symbol feeds with minimal degradation.
Resource Efficiency: Typical runtime stays around 150–250 MB RAM and under 20% CPU per active worker.
Error Handling: Uses exponential backoff, structured logs, state snapshots, and automatic recovery for transient failures.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
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"Exceptional results, clear communication, and flawless delivery. Syed
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