ajeet

This one is best to start: https://pythonfintech.com/

In machine learning and AI development, predictive baselines often begin at a coin flip level (50%). Model successfully improved upon this, achieving a 57% directional accuracy rate. To further optimize performance, I integrated failure level logic to trigger opposing trades when an initial prediction invalidates. Additionally, as a risk mitigation safeguard, trades are not executed solely on predictive signals. Instead, execution relies on a 1-minute chart indicator that tracks divergence for precise entry points, placing the stop-loss at the most recent swing high or low. The end-to-end management of this trade setup is automated via Gemini.

you can evaluate decisions by Qwen-3 here. https://t.me/passion_trades (Don't go by timing mentioned in message, just follow time of message)

Introducing BARFI: My Fully Automated, Multimodal AI Trading System for XAUUSD (Qwen 2.5/3 + Gemini 3.1 + NT8/MT5) Hey everyone, I wanted to share a project I've been building and backtesting over the last few months. It's now fully automated from market analysis to trade execution. I call it BARFI (Bullion Analytics Research & Forecasting Intelligence). The core goal of BARFI is to solve a massive problem in algorithmic trading: combining raw data (OHLCV) with visual context (Footprint/Order Flow charts) to understand market regimes. Here is exactly how the architecture works, from ingestion to execution. 1. The Data Foundation & Local FineTuning Before automating live data, I built a heavy training dataset to teach a local Small Language Model (SLM) what specific market regimes look like: The Dataset: 1,000+ intraday 5-minute order flow footprint charts and over 1 million rows of historical OHLCV data in CSV format. The Local Brain: I used this data to train/finetune an open source Qwen 2.5 model. Its sole job is pattern matching n recognizing current market regimes by matching live setups against my historical database. 2. The Hourly Ingestion Pipeline Every hour, on the hour, a dual platform bridge triggers: NinjaTrader 8 (NT8): Automatically takes and saves a screenshot of the live 5-minute footprint/order flow chart. MetaTrader 5 (MT5): Automatically exports the last 500 rows of 5min OHLCV data. 3. Layer 1: Local Screening (The SLM) Instead of throwing raw data blindly at an expensive cloud API, BARFI uses the local finetuned Qwen 2.5 model first. The local SLM ingests the new hourly data. It scans the historical database to find the 5 most mathematically and visually matched historical scenarios. It compiles these 5 scenarios into an initial structured analysis report. 4. Layer 2: Deep Reasoning (Qwen 3-Max-Thinking) Once the local report is ready, BARFI calls the Qwen 3-Max-Thinking API. This layer handles the heavy cognitive lifting. Inputs sent: The live 5-minute footprint screenshot + the 500 rows of OHLCV data + the local SLM’s 5 scenario matching report. The Output: Qwen 3-Max conducts an in-depth reasoning analysis, predicts the trend for the upcoming 1 hour, and establishes exact, decisive price levels (Support, Resistance, Invalidation). 5. Layer 3: Agentic Execution & Management (Gemini 3.1 Flash Lite) Once Qwen 3-Max outputs the trend and levels, an agentic AI workflow takes over utilizing Gemini 3.1 Flash Lite for fast, low-latency execution: Broadcast: It formats the trend and levels and instantly sends a broadcast to a private Telegram channel via API. Execution: It parses the decisive levels, calculates risk management metrics (position sizing, risk/reward ratio), and triggers a live trade directly into MT5. Trade Management: Gemini doesn't just "fire and forget." The agent stays active, watching the trade in real-time on MT5 until either the Target Profit or Stop Loss is met. Why This Hybrid Approach Works What I love most about this setup is the efficiency vs. capability balance. Running everything through a massive thinking model every hour is slow and expensive. By utilizing a highly specialized, locally trained model to do the initial "heavy lifting" filter, the cloud model only has to reason across highly curated, relevant data. So far, the multimodal approach (giving the AI both the visual footprint chart and the hard numbers of the OHLCV) has vastly outperformed my old numbers, only models, especially on XAUUSD where volume profile and order flow shifts dictate the intraday trend. Would love to hear your thoughts on this multi model architecture!

That’s a solid take. If you’re the kind of trader who likes to get hands on with order flow, ATAS really shines. The built-in footprint charts are sharp, and the default settings just work, plus the free tier is hard to pass up. But let’s be real,these platforms are built so differently when you look at where you want your trading to go in the long run. ATAS is basically a great visual toolkit. On the other hand, NinjaTrader 8 feels more like a playground for developers. Since NT8 hooks right into C# and the .NET library, you can create automated workflows and analysis tools that ATAS just isn’t built to handle. With NT8, you’re not stuck drawing indicators and staring at charts. You can actually write custom scripts to do things like connect your trading setup to Python or AI models, snag chart data automatically, crunch numbers behind the scenes, and feed those results back into your trading system, Or throw together multilayered trading bots that hunt for opportunities across tons of instruments at the same time,all running your own logic. So, yeah,ATAS works wonders if you want to act fast and make manual decisions. But if you’re aiming to scale up and automate, or tap into the latest AI analysis, NT8’s open-source flexibility is a game changer. Good luck on the switch. In the end, it’s just about finding the right tool for how you like to trade.

before using any account patch your software for new account and dont try rithmic on weekend.

This indicator functions are much similar to Ninzacator's Volume Tank Army.

Very old pack, shared by many including me.

Anyone can experiment with a lightweight yet relatively robust architecture of TCN-Attention for financial market prediction (primarily trend direction and next-bar price estimation). we can sharpen the setup on potential overfitting, validation strategies, and feature engineering. Dataset: 60,000+ rows of 1h OHLCV data (any scrip, but the framework is generic). Train / val / test split: 70/15/15 (chronological, no shuffle). Architecture can be: Input (lookback=128) → Conv1D (filters=64, kernel=8, dilation=1) → TCN block (dilations=1,2,4,8,16, residual) → Multi-head Attention (query from last timestep, key/value from full sequence) → Global Avg Pooling (or flatten) → Dense(32, dropout=0.3) → Output: - Regression: price change % (tanh scaled) - Classification: direction (up/down, sigmoid) Loss: combined MSE + binary crossentropy (weighted). Results that are achievable: Directional accuracy: ~54–56% (varies by pair/period, not great but above coin flip). Regression MAE (price change %): ~0.32% for crypto, 0.09% for forex. Sharpe of strategy based on model signals (backtest): ~1.1 (after 0.1% slippage). Go and google similar ML models...

I'll be honest, most of the technical side like how the .dll resources are bundled, is a bit over my head! I’m not very tech-savvy, so I just look at how the tools actually perform. I ran both files on a non-EDU version of NinjaTrader, and the results and outputs were exactly the same. Based on that, I concluded the files were identical. Looking for your upcoming 'larger pack'.

The files are absolutely identical and I have edu NT8, I indicated edu NT8 specifically.

Try MZpack

Full pack has already been shared.

One from MZpack is also good