TradeMaster and OpenAlgo can work together on your VPS, but they do not connect out of the box with a single toggle.TradeMaster handles the intelligence layer (training AI and generating buy/sell signals), while OpenAlgo acts as your execution layer (communicating with your broker API to place orders). How the Workflow Operates[ Market Data Feed / OpenAlgo Historify ]
│
▼
[ TradeMaster RL Agent (Python) ] ──> Decides Action (Buy/Sell/Hold)
│
▼
[ OpenAlgo REST API / Python SDK ] ──> Places Order (HOST + API_KEY)
│
▼
[ Broker API Execution ]
Model Training (Offline/Colab/PC): Reinforcement Learning models (like DeepScalper or PPO) require significant compute to train. It is generally best to train the weights on a GPU or Google Colab, then transfer the lightweight trained model file (.pth or .pkl) to your VPS.Inference (Live on VPS): A lightweight Python script runs on your VPS. Every candle or tick, it feeds the latest price features to your trained TradeMaster model to predict an action.Execution via OpenAlgo: When the model outputs a trade signal, the script sends an order payload to your local OpenAlgo instance (http://localhost:5000/api/... or via the openalgo Python SDK) to trigger the broker order.Key Considerations for Your VPS SetupCompute Constraints: Standard CPU VPS instances handle live inference (reading model predictions) fine, but attempting to train RL models from scratch on a basic VPS will max out CPU/RAM and cause lag.Data Formatting: TradeMaster expects normalized historical data arrays (e.g., standard OHLCV or custom indicators). You will need a simple data bridge to feed your market data directly into the model's observation space.Complexity Level: Unlike rule-based setups (such as moving average crossovers or price action breaks), RL models operate dynamically. Testing extensively in OpenAlgo's Sandbox mode before live deployment is essential. pls search and discuss with me - i want to use ths on our vps
2026-08-18 23:54
ASSISTANT (hermes, deepseek-v4-flash)
2026-08-18 23:55
TOOL (hermes, deepseek-v4-flash)
[Tool: web_search]
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"description": "# TradeMaster: An RL Platform for Trading\n[Permalink: TradeMaster: An RL Platform for Trading](https://github.com/TradeMaster-NTU/TradeMaster#trademaster-an-rl-platform-for-trading)\n\n[](https://github.com/TradeMaster-NTU/TradeMaster/blob/main/figure/Logo.png)\n\n[](https://www.python.org/downloads/release/python-3916/)[](https://github.com/TradeMaster-NTU/TradeMaster/blob/1.0.0/Platform)[](https://github.com/TradeMaster-NTU/TradeMaster/blob/1.0.0/License)[](https://trademaster.readthedocs.io/en/latest/)[](https://github.com/TradeMaster-NTU/TradeMaster/stargazers)\n\nTradeMaster is a first-of-its kind, best-in-class **open-source platform** for **quantitative trading (QT)** empowered by **reinforcement learning (RL)**, which covers the **full pipeline** for the design, implementation, evaluation and deployment of RL-based algorithms.\n\nPlease send him an email if you are interested!\n\n## Overview\n[Permalink: Overview](https://github.com/TradeMaster-NTU/TradeMaster#overview)\n\n[](https://github.com/TradeMaster-NTU/TradeMaster/blob/1.0.0/figure/architecture.jpg)\n\n## Tutorial\n| Algorithm | D
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2026-08-18 23:55
TOOL (hermes, deepseek-v4-flash)
[Tool: web_search]
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"description": "# TradeMaster: An RL Platform for Trading\n## File Structure\n```\n| TradeMaster\n| ├── configs\n| ├── data\n| │ ├── algorithmic_trading\n| │ ├── high_frequency_trading\n| │ ├── order_excution\n| │ └── porfolio_management\n| ├── deploy\n| │ ├── backend_client.py\n| │ ├── backend_client_test.py\n| │ └── backend_service.py\n| │ ├── backend_service_test.py\n| ├── docs\n| ├── figure\n| ├── installation\n| │ ├── docker.md\n| │ ├── requirements.md\n| ├── tools\n| │ ├── algorithmic_trading\n| │ ├── data_preprocessor\n| │ ├── high_frequency_trading\n| │ ├── market_dynamics_labeling\n| │ ├── missing_value_imputation\n| │ ├── order_excution\n| │ ├── porfolio_management\n| │ ├── __init__.py\n| ├── tradmaster\n| │ ├── agents\n| │ ├── datasets\n| │ ├── enviornments\n| │ ├── evaluation\n| │ ├── imputation\n| │ ├── losses\n| │ ├── nets\n| │ ├── preprocessor\n| │ ├── optimizers\n| │ ├── pretrained\n| │ ├── trainers\n| │ ├── transition\n| │ ├── utils\n| │ └── __init__.py\n| ├── unit_testing\n| ├── Dockerfile\n| ├── LICENSE\n| ├── README.md\n| ├── pyproject.toml\n| └── requirements.txt\n```\n\n## About\nTradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning 🔥 ⚡ 🌈",
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2026-08-18 23:55
TOOL (hermes, deepseek-v4-flash)
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2026-08-18 23:55
TOOL (hermes, deepseek-v4-flash)
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2026-08-18 23:55
ASSISTANT (hermes, deepseek-v4-flash)
Searched and verified against the actual repo/docs. TradeMaster (TradeMaster-NTU on GitHub, NeurIPS 2023 paper) is real and its design matches your pasted workflow — but there are 4 things you should know before we commit anything to the VPS. Here's the honest picture:
1. WHAT IT ACTUALLY IS
TradeMaster is a research platform (NTU), not a trading bot. It covers the full RL pipeline: data preprocessor -> environments (AT/order-execution/portfolio) -> agents (DeepScalper, DeepTrader, SARL, EIIE, OPD, ETTO, Investor-Imitator) -> trainers -> evaluation -> deploy/backend_service.py (an inference server — exactly the "thin VPS inference loop" piece). DeepScalper is just one of ~7 models; it was built/tested on BTC 1-min data.
2. WHAT I VERIFIED ABOUT RUNNING IT HERE
- Not on PyPI (`pip install trademaster` fails) — you clone and build from source.
- Main branch is mid-rewrite: its pyproject.toml is literally a placeholder stub. Expect to fight the codebase, not install it cleanly.
- Needs PyTorch + gym etc. VPS has python3.11 in ~/.local (good — torch-friendly) and system python3.13. No GPU here, 6 cores, only ~2.9GB RAM free right now with your existing services running.
- It ships its own data downloader (Yahoo etc.) — that path is dead on arrival for us per your rule: data ONLY from Shoonya.
3. FOUR CONFLICTS WITH HOW WE RUN THINGS (your own principles, not mine)
1. Data: we must write a bridge — your existing 30s/5min DB (live-quote-filler) -> TradeMaster's expected CSV/normalized format. Doable, it's a few hours of work, but TM's own tooling is unusable for us.
2. Fills: TM environments fill trades at candle close, no slippage/costs. By your backtest standard that's fiction — same category as brick-close fills. Any TM backtest result would need re-runs through your realistic-fill engine before you trust a number.
3. WYSIWYG: RL is a black box. It can't tell you what/when/why it trades. That directly collides with your dashboard philosophy — you nuked a dashboard that didn't tell the complete story. A live RL signal with no explainable story will feel the same way.
4. Compute: training DeepScalper/PPO on this CPU box = days to weeks, and it would starve your running bots of RAM. Inference is trivial (milliseconds per bar).
4. WHAT THE PASTED WORKFLOW GETS RIGHT
Train offline on your PC/Colab with GPU, ship the .pth to the VPS, and run a thin inference loop: features from our own DB -> model -> action -> OpenAlgo REST (your local flask instance) -> Shoonya. The OpenAlgo leg is the easy 10% — that pattern already exists in your bots, and sandbox analyze_mode covers testing before live.
5. MY REAL RECOMMENDATION
Don't replace your rule-based bots with RL. Use RL as a gate/confirmation on top of the renko-vidya or 921-swing signals first — model says "no trade" on weak setups, bots still own entries/exits. Small blast radius, keeps the story explainable, and you can judge the model's edge with real money at stake before trusting it solo.
2026-08-18 23:56
TOOL (hermes, deepseek-v4-flash)
[Tool: clarify]
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2026-08-18 23:58
ASSISTANT (hermes, deepseek-v4-flash)
No reply needed from you to move this forward — the discussion stands as delivered above. Since you didn't pick a path, I'm not building anything yet; here's the one thing that decides the whole architecture whenever you're ready:
1. THE DECIDING QUESTION: where will training run? Your PC/Colab with a GPU (recommended — matches the pasted workflow, VPS never trains) or a small training job on this VPS anyway (6 cores, ~3GB free — possible for a tiny model, painful for DeepScalper).
2. NEXT STEP WHEN YOU SAY GO: I'd clone the repo, read the DeepScalper paper + the AT environment source, and come back with exactly what observation state it needs (price features, window size, action space) mapped against what your 30s/5min Shoonya DB already has — so the data bridge is the only real build, everything else bolts onto your existing OpenAlgo pattern.
3. The rest of the workflow is already settled: model weights (.pth) land in a bot folder, thin inference loop reads features from our own DB, action -> your local OpenAlgo flask -> Shoonya, sandbox analyze_mode first, and the first live version rides as a gate on an existing bot (my recommendation) or standalone per your call.
Reply with a path (1-5 from the options) or just "go" for the deep-dive, and I'll start.