Refactor Label Y: Market Order Entry Eliminates NO_FILL

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Refactor Label Y: Market Order Entry Eliminates NO_FILL\n\nHey there, fellow data enthusiasts and trading strategists! Today, we're diving deep into a *super important* refactoring task that's going to make our algorithmic trading research much more robust, reliable, and frankly, a whole lot less headache-inducing. We're talking about revamping how we calculate our `Y` labels – that crucial target variable our models learn to predict – by switching from a simulated _limit order_ entry to a simulated _market order_ entry. The main goal? To completely **eliminate those pesky 'NO_FILL' scenarios** that complicate our data and model training. This isn't just a small tweak; it's a fundamental shift that simplifies our entire machine learning pipeline, ensures 100% data utilization, and ultimately helps us build more effective trading strategies. So, buckle up, guys, because we're about to make our data cleaner and our models happier!\n\n## The Headache: Why 'NO_FILL' Samples Are a Problem\n\nAlright, let's talk about the current situation and why we need to change it. Our existing `03_build_labels.py` script, which is responsible for generating our `Y` labels, simulates a **limit order** entry strategy. Specifically, it uses the `T-1 Close` price as our entry point (`p`). Now, in the real world, placing a limit order at the previous day's close price means there's _no guarantee_ it will actually execute when the market opens the next day. If the price gaps up significantly, your limit order might just sit there, unfilled, while the market moves on. This scenario, which we've dubbed "Scenario B (Unfilled)," creates what we call "NO_FILL" samples in our dataset.\n\nThese "NO_FILL" samples are a real pain for a few key reasons. First, they represent _data loss_. When we process our data in `04_clean_and_merge_data.py`, these unfilled samples are simply discarded. Imagine training a machine learning model; every single data point is valuable, and throwing away samples, especially if they represent a particular market condition, can lead to a less comprehensive understanding for our model. It's like trying to teach someone about driving, but only showing them how to drive on perfectly flat, sunny roads, ignoring all the tricky, rainy, or hilly conditions. Your model ends up with blind spots, and that's not what we want for something that's supposed to handle the unpredictable world of stock markets.\n\nSecond, the presence of "NO_FILL" samples introduces **complexity and ambiguity** into our model's task. If our model is trying to predict a `Y` value, but some of the potential `Y`s simply don't materialize because the entry order wasn't filled, then the model is trying to learn from a world where outcomes are conditional not just on price movements, but also on the *likelihood of an order filling*. This adds an extra layer of implicit prediction that makes the primary task of predicting price change much harder. We want our model to focus on the signal, not on the nuances of order execution. By eliminating `NO_FILL`s, we simplify the problem for our model, allowing it to concentrate purely on predicting the _potential profit or loss_ if an entry were guaranteed. This creates a much cleaner learning signal and a more straightforward objective for our predictive analytics, making our model training process far more efficient and the resulting models much more interpretable and robust. Think of it as giving our model a clear, unambiguous goal, rather than a fuzzy, multi-faceted one.\n\n## Embracing Market Orders: A Simpler Path Forward\n\nSo, what's our solution to this "NO_FILL" conundrum? We're making a bold, but incredibly smart, move: simulating **market order** entries. Instead of trying to get in at `T-1 Close` with a limit order that might or might not fill, we're going to assume we always get into a trade at the **opening price of the current day (T Open)**. This is a game-changer, guys, because by definition, a market order is designed to execute immediately at the best available price. While in reality, there can be slight slippage, for the purpose of backtesting and simplifying our model's task, assuming an entry at `T Open` ensures a **100% fill rate**. This means _no more_ "NO_FILL" samples, _no more_ discarded data, and _no more_ ambiguity for our predictive model.\n\nThis shift isn't just about convenience; it's about providing a **cleaner, more consistent dataset** for our machine learning algorithms. When every potential trade has a guaranteed entry and exit, our model can focus solely on predicting the _magnitude and direction_ of the price movement, represented by `Y`. It no longer has to implicitly learn the probability of an order filling, which was an unnecessary distraction. This directly translates to a more focused and, hopefully, more accurate model. By removing the execution uncertainty from the *label generation process*, we're creating a more ideal training environment. Our model can then develop a stronger understanding of price dynamics without the noise introduced by conditional fills. Furthermore, this approach aligns well with many real-world short-term trading strategies where traders prioritize execution over a specific price point, especially when looking for momentum or overnight gaps. It essentially abstracts away the granular details of order book dynamics and slippage during backtesting, allowing us to evaluate the core hypothesis of our strategy more effectively. This simplifies our entire research pipeline, from data preparation to model evaluation, giving us greater confidence in the signals our models generate. We're moving towards a system where our model learns to predict _potential market movements_ with the assumption that we can always act on them, which is a powerful simplification for initial strategy development and evaluation.\n\n## Hands-On: Modifying `03_build_labels.py`\n\nNow, let's get down to the nitty-gritty and see how we're actually implementing this change in our code. We'll be focusing on `03_build_labels.py`, specifically two key functions: `calculate_label_inputs` and `determine_fill_status_and_calculate_y`. These modifications are designed to seamlessly transition our label generation to the market order simulation, ensuring every potential trade generates a calculable `Y` value.\n\n### Step 1: Adjusting Entry and Exit Prices in `calculate_label_inputs`\n\nThe first major change happens within our `calculate_label_inputs` function. This is where we define what constitutes our entry price (`p`) and our exit price (`p_exit`). Previously, we were using `T-1 Close` for `p` and, funnily enough, a mistakenly implemented `T Open` for `p_exit`. This setup was inherently flawed for a clear 1-day holding period strategy. With our new market order philosophy, we're making these definitions crystal clear and consistent.\n\nWe're updating the code as follows:\n\n```python\n# Located within calculate_label_inputs function\n\n# --- Old logic (T-1 Close entry) ---\n# symbol_data['p'] = symbol_data['Close']\n# symbol_data['p_exit'] = symbol_data['Open'].shift(-1) # This was actually T Open\n\n# --- New logic (T Open entry, T+1 Open exit) ---\nsymbol_data['p'] = symbol_data['Open'].shift(-1) # p = T Open\nsymbol_data['p_exit'] = symbol_data['Open'].shift(-2) # p_exit = T+1 Open\n```\n\nLet's break down what `symbol_data['Open'].shift(-1)` and `symbol_data['Open'].shift(-2)` actually mean. When we use `.shift(-1)` on a Pandas Series, we're essentially looking at the *next* row's value. So, for a row representing `T-1`, `symbol_data['Open'].shift(-1)` will give us the `Open` price of day `T`. This becomes our new entry price `p` for a trade that we are considering initiating based on `T-1`'s data. This makes perfect sense for a market order executed at the beginning of day `T`. Similarly, `symbol_data['Open'].shift(-2)` will fetch the `Open` price of day `T+1`, which then becomes our `p_exit`. This establishes a consistent **one-day holding period**: we enter at `T Open` and exit at `T+1 Open`. This clear definition of entry and exit points is absolutely crucial for generating a `Y` label that accurately reflects the profit/loss of a consistent, market-order-based strategy. This standardization removes any ambiguity in calculating our target variable, providing a much clearer signal for our machine learning models to learn from. It’s all about creating a robust and understandable framework for backtesting that closely mirrors a specific and common trading style, simplifying the subsequent analytical steps and strengthening the overall reliability of our research outcomes.\n\n### Step 2: Simplifying Fill Logic in `determine_fill_status_and_calculate_y`\n\nNext up, we're heading over to the `determine_fill_status_and_calculate_y` function. This is where the magic happens regarding simplifying our fill logic. With our commitment to market orders, the concept of an order not filling becomes obsolete. We're assuming 100% execution, every single time. This means we can strip out all the complex checks that were previously trying to figure out if a limit order would have filled based on the intraday price range.\n\nHere's a simplified look at the updated logic:\n\n```python\ndef determine_fill_status_and_calculate_y(labels_df, data_60m):\n # ... (data_60m is no longer used for fill status logic)\n \n results = []\n for (symbol, t_minus_1_timestamp), row in labels_df.iterrows():\n p = row['p']\n p_exit = row['p_exit']\n vol = row['vol'] # Assuming 'vol' here refers to a volatility measure like ATR\n\n fill_status = 'FILLED' # Always filled now!\n y = np.nan # Default to NaN\n\n try:\n # Calculate Y only if all necessary data is valid and vol > 0\n if pd.notna(p) and pd.notna(p_exit) and pd.notna(vol) and vol > 0:\n y = (p_exit - p) / vol\n except Exception as e:\n print(f"An error occurred for {symbol} at {t_minus_1_timestamp}: {e}")\n pass\n\n results.append({\n 'asset': symbol,\n 'T-1_timestamp': t_minus_1_timestamp,\n 'Y': y,\n 'Fill_Status': fill_status\n })\n \n final_labels = pd.DataFrame(results).set_index(['asset', 'T-1_timestamp'])\n print("Fill status and Y labels calculated (Market Order simulation).")\n return final_labels\n```\n\nAs you can see, we're completely removing any reliance on `data_60m` for fill status determination. This means no more checking `min_low_t` or any other intraday price metrics to see if an entry would have occurred. Instead, `fill_status` is now **hardcoded to `'FILLED'`**. This is the core of our simplification! The calculation of `Y` itself becomes much more straightforward: `Y = (p_exit - p) / vol`. The `vol` here is typically a volatility measure, like Average True Range (ATR) from `T-1`, used to normalize the profit/loss, making `Y` a volatility-adjusted return. This is crucial because raw price differences can vary wildly depending on the stock's price level; normalizing by volatility makes `Y` comparable across different assets and different periods. We still include checks for `pd.notna(p)`, `pd.notna(p_exit)`, `pd.notna(vol)`, and `vol > 0` to ensure that we only calculate `Y` when all the required input data is actually present and valid. If any of these are missing (e.g., due to data gaps at the beginning or end of a dataset when shifting), `Y` will remain `np.nan`, which is the correct way to handle incomplete data. This streamlined logic not only makes our code cleaner and easier to understand but also removes an entire class of potential errors and inconsistencies related to partial fills or missed entries. It guarantees that if we have the necessary price data, we will always have a corresponding `Y` label, which is _awesome_ for model training stability.\n\n## What This Means for Your Research: Clearer Signals, Better Models\n\nThis seemingly small refactor has a **massive impact** on our overall research pipeline and the quality of our machine learning models. By making this switch to a market order simulation, we're not just patching a problem; we're fundamentally improving the environment our models learn in. First and foremost, we achieve **100% data utilization**. No more throwing away valuable "NO_FILL" samples. Every single observation where we have valid entry and exit prices (T Open and T+1 Open) will contribute to our model's learning. This means our models will be exposed to a richer, more complete representation of market dynamics, potentially leading to more generalized and robust predictions across various market conditions, not just those where a limit order would have been filled.\n\nSecondly, we provide our models with a **cleaner and more unambiguous learning signal**. Imagine trying to predict if a coin flip will land on heads or tails, but sometimes the coin just disappears in mid-air. That's what "NO_FILL" samples were doing to our model. By ensuring every trade is filled, our `Y` label becomes a direct and consistent measure of profit/loss potential for a guaranteed trade. The model can now focus its energy entirely on the core task of identifying patterns that lead to positive `Y` values, without the added complexity of predicting execution feasibility. This simplification allows the model to build stronger, more direct correlations between our input features and the target outcome, leading to more accurate predictions of *price direction and magnitude* rather than being distracted by the nuances of order book liquidity or intra-day price fluctuations relative to a limit price.\n\nFurthermore, this change **simplifies the entire model training and evaluation process**. Debugging becomes easier because we don't have to account for missing labels due to fill status. Our performance metrics will be based on a consistent universe of trades. It also streamlines our backtesting. When we backtest strategies, we want to evaluate their effectiveness assuming we can execute our ideas. This market order simulation closely aligns with that desire, providing a more optimistic but consistent baseline for performance. While real-world slippage will always exist, this refactor helps us assess the *true predictive power* of our models, independent of specific order types and their execution uncertainties during initial development. This is a critical step in building confidence in our underlying strategy logic before moving to more complex real-world simulations. Essentially, we're creating a robust, clear, and stable foundation for all future research, ensuring that our efforts are directed towards truly predictive features rather than fighting with data inconsistencies and execution complexities.\n\n## Verifying the Changes: Acceptance Criteria\n\nAlright, guys, no refactor is complete without a solid set of acceptance criteria to make sure everything works as expected. We need to be confident that our changes deliver on their promise. So, once you've implemented the modifications, here's what you need to check:\n\n* **Successful Execution of `03_build_labels.py`**: The script should run from start to finish without any errors and successfully produce the `labels_Y.parquet` file. This is our primary output, and its creation is non-negotiable.\n* **`Fill_Status` Consistency**: Open up that `labels_Y.parquet` file (you can use pandas to read it) and inspect the `Fill_Status` column. Every single entry in this column **must contain `'FILLED'`**. There should be _no_ other values, especially no `'NO_FILL'`s. This is the ultimate proof that our market order simulation is working as intended and we've banished the "NO_FILL" nightmare.\n* **`Y` Calculation Accuracy**: The values in the `Y` column should be calculated correctly based on our new logic. That means `Y` should be equivalent to `(T+1 Open - T Open) / (T-1 ATR)`. You might want to pick a few sample rows and manually verify the calculation using your raw price data to ensure the formula is applied correctly. This step is crucial for data integrity.\n* **`04_clean_and_merge_data.py` Log Confirmation**: When you run `04_clean_and_merge_data.py` (which processes the `labels_Y.parquet` file), keep an eye on the console output. You should see a log message explicitly stating something like: "**共刪除 0 筆 'NO_FILL' 樣本**" (meaning "0 'NO_FILL' samples deleted"). This message is the final confirmation that our downstream processing is no longer encountering and discarding any unfilled entries, indicating a perfectly clean dataset. If you see any number other than zero here, something's still amiss, and it's time to re-evaluate your changes in `03_build_labels.py`. These checks ensure that our pipeline is robust, our data is clean, and our models are set up for success.\n\n## Conclusion\n\nAnd there you have it, folks! This refactor, moving from simulated limit orders to simulated market orders for our `Y` label generation, is more than just a code change; it's a strategic enhancement to our entire algorithmic trading research framework. By eliminating those frustrating "NO_FILL" samples, we're creating a **cleaner, more consistent, and ultimately more effective** environment for our machine learning models to thrive. We're ensuring 100% data utilization, providing a crystal-clear learning signal, and simplifying our entire pipeline from data preparation to model evaluation. This means less debugging, more robust models, and a faster path to discovering profitable trading strategies. So, let's get these changes implemented, verify them with our acceptance criteria, and take our research to the next level. Happy coding, and here's to building some seriously smart trading bots!