{"id":25042487,"url":"https://github.com/s1dewalker/futures-trading","last_synced_at":"2026-02-17T19:01:43.527Z","repository":{"id":249803274,"uuid":"697560606","full_name":"s1dewalker/Futures-trading","owner":"s1dewalker","description":"A glimpse of Futures trading. 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Keeping an Eagle Eye on Australian Fixed Income market\n\n![](Screenshots/DB4.PNG)\n\u003cbr/\u003e\n*STIR: 30-day Interbank Cash Rate Futures, 90-day accepted Bill Futures, their spreads, flies, de-flies, condors, other combinations and curve structures.* \u003cbr/\u003e\n*BONDS: 3-year bond, 10-year bond, yield curve.* \u003cbr/\u003e\n*Creating this dashboard helped to view the entire Australian Fixed Income futures market in one screen.*\n\u003cbr/\u003e\u003cbr/\u003e\n\n### RISK SCENARIO ANALYSIS - Look at the Range of Outcomes and Be Prepared for it \n\n![](Screenshots/sa2.PNG)\n\u003cbr/\u003e\n*Case scenarios for front contracts | Meeting impacts on contracts | Contract ranges | Curve movement | Risk-reward ratio* \u003cbr/\u003e\n\n*This helped to input basis point expectations for each RBA meeting and check the pricing for different cases, which gave ranges, risk-reward for contracts and combinations. Bottom left side had meeting impact section, which showed meeting impacts on the selected contract or strategy.* \u003cbr/\u003e *This showed which trades not to take, more than, which trades to take.* \u003cbr/\u003e\n\n*\"Sometimes, not taking a trade is a trade itself.\"*\n\u003cbr/\u003e\u003cbr/\u003e\n\n### PERFORMANCE ANALYSIS - Changing the Game with Metrics and Stats\n![](Screenshots/pa_final.JPG)\n\u003cbr/\u003e\n*Analyzing different setups and contracts | Net PnL, Participation and Lot sizing \u003cbr/\u003e\nWins vs Losses | Viewing wins \u0026 losses objectively* \u003cbr/\u003e\n\n*Separating wins and losses helped check the nature of trading strategies objectively. This helped to be defensive on losing (or, not so profitable) strategies and push on the better ones, improving consistency.*\n\u003cbr/\u003e\n\n*Consistency is not just about making profitable trades every single time. It is about growing a discipline approach to trading that generates reliable results over a long period of time.* \u003cbr/\u003e\n\n*__Consistency focus:__* \u003cbr/\u003e\n\n- *Market analysts focused on consistency prioritize the reliability and stability of their performance over time. They aim to generate steady returns while minimizing the impact of losses and market volatility.*\n- *While consistency focused analysts may not always achieve the highest returns on individual transactions, they aim to maintain a steady and reliable track record of profitability. In turn such an approach can lead to more predictable and manageable outcomes over the long term.*\n\n\u003cbr/\u003e\n\n### AUTOMATED TRACKING FI METRICS\u003cbr/\u003e\n\n\u003cimg src=\"Screenshots/macrobp.png\" alt=\"Description\" width=\"800\"\u003e\n\n\u003cimg src=\"Screenshots/macromet.JPG\" alt=\"Description\" width=\"800\"\u003e\n\n*Automated recording metrics like expectations for the next 10 RBA meetings, yield curve, terminal rate, around volatile events using Excel Macros.* \u003cbr/\u003e\n\u003cBR/\u003e\u003cBR/\u003e\n\n# QUANTITATIVE ANALYSIS (QA) \u0026 MODELS\u003cbr/\u003e\n### Statistical Analysis | Value at Risk (VaR) \n\n\u003cimg src=\"Screenshots/var.JPG\" alt=\"Description\" width=\"500\"\u003e \n\n#### [View Python code for statistical analysis](https://github.com/s1dewalker/Futures-trading/blob/main/Stats.ipynb)  \n\n### Random Walk Simulation for Simulation VaR\n\n\u003cimg src=\"Screenshots/monte_carloSim.JPG\" alt=\"Description\" width=\"500\"\u003e \n\n#### [View Monte Carlo Simulation](https://github.com/s1dewalker/Futures-trading/blob/main/Quantitative-Analysis-(QA)/A_random_walk_simulation.ipynb)\n\n### Correlation\n\u003cimg src=\"Screenshots/correlation_24.JPG\" alt=\"Description\" width=\"500\"\u003e\n\n### Machine Learning (k-means clustering) to find market states \n\nSteps: \u003cbr/\u003e\n1. Data preparation: cleaning, transformation \u003cbr/\u003e\n2. Create features: like high-low, hl/volume, 5 day rolling volatility, etc and get them in a separate dataframe \"X\"\n3. Normalize \"X\" (as some features might dominate due to larger scale): use `MinMaxScaler` function\n4. Find market states w/ k-means clustering: find best \"k\" with WCSS method with `inertia_`. Fit the model with `KMeans`. Predict clusters with `.fit_predict` on \"X\".\n\n#### [View sample clustering in Python](https://github.com/s1dewalker/Futures-trading/blob/main/Quantitative-Analysis-(QA)/qtnb3-states.ipynb)\n\u003cbr/\u003e\n\n### Simple Markov Model to predict market states\n\n\u003cimg src=\"Screenshots/tr_mat.JPG\" alt=\"Description\" width=\"300\"\u003e\n\n*Applications:* \u003cbr/\u003e\n- *Prediction: Predict the next state of the market based on the current state (i.e., forecasting the market's behavior).*\n- *Optimization: Use the Markov Model for portfolio optimization, where states represent different market conditions, and transitions model how the market shifts.*\n- *Risk Assessment: Assess the risk of being in a certain state at a future time.*\n\n#### [View sample Model in Python](https://github.com/s1dewalker/Futures-trading/blob/main/Quantitative-Analysis-(QA)/MARKOV_MODEL_4.ipynb)\n\n\u003cbr/\u003e\n\n### Trade Journal\u003cbr/\u003e\n\n\u003cimg src=\"Screenshots/TJ2.JPG\" alt=\"Description\" width=\"800\"\u003e\n\n*Maintaining a journal for events, trade setup, risk management, worst case losses, PnL, observations, perceptions, and strategy updates.* \u003cBR/\u003e\n***Knowing the risks before taking them.*** \u003cbr/\u003e\n*[View Trade Journal Analysis in Python](https://github.com/s1dewalker/Airbnb-listings-NYC/blob/main/Airbnb%20correlation.ipynb](https://github.com/s1dewalker/Futures-trading/blob/main/trade_journal_analysis/tja.ipynb))* \u003cbr/\u003e\n\n\u003cBR/\u003e\u003cBR/\u003e\n# RESILIENT AND DEFENSIVE ACCOUNT MANAGEMENT SYSTEM\u003cbr/\u003e\n*Balance sheet was mostly Conservative and Risk Averse.*\n\n- #### Lot sizing\n\u003cimg src=\"Screenshots/lot_sizing.JPG\" alt=\"Description\" width=\"800\"\u003e\n\n- #### Allocation\n\u003cimg src=\"Screenshots/allocation.JPG\" alt=\"Description\" width=\"800\"\u003e\n\n\u003cBR/\u003e\u003cBR/\u003e \n\n# RESULTS\n![](Screenshots/pat23.PNG)\n\n\u003cbr/\u003e\u003cbr/\u003e\n\n\n\n##### [LinkedIn](https://www.linkedin.com/in/sujay-bhaumik-d12/) | s1dewalker23@gmail.com | [Discussions](https://github.com/s1dewalker/Futures-trading/discussions/1) | [Research Works](https://github.com/s1dewalker/Research-Works)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fs1dewalker%2Ffutures-trading","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fs1dewalker%2Ffutures-trading","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fs1dewalker%2Ffutures-trading/lists"}