From 552291a10745e2ca36af6a4126cc9a163f7e0f42 Mon Sep 17 00:00:00 2001 From: nelanbu Date: Tue, 20 Feb 2024 23:56:19 +0100 Subject: [PATCH] btc price prediction pipeline commit --- .github/workflows/daily_ingestor.yml | 30 + .gitignore | 3 + Bitcoin Price Prediction Pipeline.pdf | Bin 0 -> 313340 bytes README.md | 64 +- app.ipynb | 537 ++++++++++++++ app/app.py | 110 +++ app/requirements.txt | 5 + btc_model/btc_model.h5 | Bin 0 -> 414024 bytes feature-pipeline-daily.py | 60 ++ feature-pipeline.ipynb | 341 +++++++++ images/pipeline.jpeg | Bin 0 -> 81786 bytes lambda_daily_hopsworks/Dockerfile | 17 + lambda_daily_hopsworks/requirements.txt | 10 + lambda_daily_hopsworks/src/main.py | 60 ++ predicted_prices.png | Bin 0 -> 433974 bytes requirements.txt | 5 + training-pipeline.ipynb | 950 ++++++++++++++++++++++++ 17 files changed, 2191 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/daily_ingestor.yml create mode 100644 .gitignore create mode 100644 Bitcoin Price Prediction Pipeline.pdf create mode 100644 app.ipynb create mode 100644 app/app.py create mode 100644 app/requirements.txt create mode 100644 btc_model/btc_model.h5 create mode 100644 feature-pipeline-daily.py create mode 100644 feature-pipeline.ipynb create mode 100644 images/pipeline.jpeg create mode 100644 lambda_daily_hopsworks/Dockerfile create mode 100644 lambda_daily_hopsworks/requirements.txt create mode 100644 lambda_daily_hopsworks/src/main.py create mode 100644 predicted_prices.png create mode 100644 requirements.txt create mode 100644 training-pipeline.ipynb diff --git a/.github/workflows/daily_ingestor.yml b/.github/workflows/daily_ingestor.yml new file mode 100644 index 0000000..32aa494 --- /dev/null +++ b/.github/workflows/daily_ingestor.yml @@ -0,0 +1,30 @@ +name: btc-daily-data-ingestor + +on: + push: + branches: main + + +jobs: + test_schedule: + runs-on: ubuntu-latest + steps: + - name: checkout repo content + uses: actions/checkout@v2 + + - name: setup python + uses: actions/setup-python@v2 + with: + python-version: '3.10.10' + + - name: install requirements + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + - name: execute python workflows from bash script + env: + HOPSWORKS_API_KEY: ${{ secrets.HOPSWORKS_API_KEY }} + run: | + python feature-pipeline-daily.py + + diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..23e8b27 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +.env +.DS_Store +.idea \ No newline at end of file diff --git a/Bitcoin Price Prediction Pipeline.pdf b/Bitcoin Price Prediction Pipeline.pdf new file mode 100644 index 0000000000000000000000000000000000000000..71f1792dcbc9f3d4a43fff4b87cd836d5762466d GIT binary patch literal 313340 zcmeFZ1yogAyFa|?PASO^k`kNl?rxA$q`MnLy1P51Q@TV%x`rD4}{@!!o zpWD1I{+BkvOoFL!{EbUxBL^ENdoWF4HpFeL9q)Hcz=yU#;>c<0Wz`Pw+|9$ z1u}8{IAQ}bKX3sOX9qGrPzGjR%+*ms#Zk}E2;7i_3KJs`{C{5$e9Qy{|AUXgr4;RL z3{;F9wLxIbfK-iK9YIo7V9^Wz*opkuN!>F8mZqVZp0JIpHn=JykR41A2N&~mAXqTg 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zb1;s44o2V@kFz=c4;whf14ll`R?FvkAaL-&k3N;5x6A2QY~0Q|j!kv&e>(oZ08XYo AM*si- literal 0 HcmV?d00001 diff --git a/README.md b/README.md index a1ebd57..69d76f7 100644 --- a/README.md +++ b/README.md @@ -1 +1,63 @@ -# btc-price-predictor \ No newline at end of file +# BTC Price Prediction + +### Problem description +The project seeks to address the challenging task of predicting daily Bitcoin prices through the development of an advanced machine learning system. +In pursuit of this objective, the primary data source will be the Binance API, a comprehensive platform that supplies historical daily Bitcoin price data. +The central challenge at hand is the forecasting of future Bitcoin prices, a problem that inherently involves unraveling intricate patterns embedded within +the historical data and incorporating pertinent features. + +Bitcoin, being a highly dynamic and volatile cryptocurrency, poses a complex prediction problem. +The goal is to create a robust model capable of capturing the underlying trends, patterns, and +dependencies in the historical price movements. By leveraging machine learning techniques, +specifically Multi Layer Perceptron and (MLP) and Long Short-Term Memory (LSTM) networks, +the project aims to provide a reliable tool for forecasting the future trajectory of Bitcoin prices. + +### Tools + +- Hopsworks : Feature store, model registry +- TensorFlow : Creating deep learning architectures. +- AWS Serverless Lambda Function : Serverless functions for data ingestion. +- Docker : Containerization of lambda functions +- EventBride : Event-Driven Function Invocation +- Google Colab : GPU access for model training +- Hugging Face Spaces : Interface Development + +### Data + +The primary data source is the Binance API, +which provides historical Bitcoin price data. +The dataset will include features such as open price, +high price, low price, close price, volume, and other relevant metrics. +Data collection will involve periodic retrieval of daily price information through the Binance API. + +### Methodology and Algorithm + +In the pursuit of accurate and effective time series prediction for Bitcoin prices, +a variety of models were explored, with notable success found in MLP +(Multi-Layer Perceptron) and LSTM (Long Short-Term Memory) architectures. +The proposed methodology centers around the application of LSTM, a +specialized type of recurrent neural network (RNN), renowned for its +proficiency in capturing long-term dependencies within sequential data. +The LSTM model will be employed to analyze historical Bitcoin price data, +leveraging its ability to discern intricate patterns and dependencies over extended temporal periods. +Feature engineering will play a crucial role in enhancing the model's understanding of the underlying dynamics, +incorporating relevant metrics such as open price, high price, low price, close price, and volume. +The hyperparameter tuning process is a pivotal step in optimizing the LSTM model's performance. +A small subset of the dataset will be dedicated to this task, facilitating the fine-tuning +of parameters such as the number of layers and nodes per layer. + +### Structure of the Project + +The project's structure is composed of four primary components: a feature pipeline, a training pipeline, a daily feature pipeline, and a user interface. The components are explained below. + +![images/pipeline.jpeg](images/pipeline.jpeg) + +The feature pipeline is responsible for data acquisition, utilizing the Binance API to retrieve data. Subsequent preprocessing tasks are performed on this data, ensuring that the features are suitably arranged before being stored in the feature store. +In the training pipeline, the LSTM model is trained using the preprocessed features from the feature store. The model is registered for future use. +The daily feature pipeline functions to update the feature store with the most recent data. It takes the current day's price, applies the same preprocessing as done in the feature pipeline, and then stores this updated information in the feature store. +Lastly, the user interface plays a critical role. It captures user input regarding the desired prediction period, specifying how many days ahead the prediction should extend. Utilizing the up-to-date data from the feature store and the registered LSTM model, the system then generates and displays a graph representing the predicted prices for the specified period. + + +### User Interface + +Our Bitcoin price prediction tool is designed to allow users to specify the number of days they wish to predict into the future via a user interface. When a user inputs a desired timeframe (in days), the tool generates a detailed graph. This graph integrates both the historical price data from the past 50 days and the predicted prices for the forthcoming days. In terms of performance, the model demonstrates commendable efficacy in short-term predictions. The user interface for this project can be accesable in [this link](https://huggingface.co/spaces/Kiwipirate/btc-predictor). \ No newline at end of file diff --git a/app.ipynb b/app.ipynb new file mode 100644 index 0000000..bab3edc --- /dev/null +++ b/app.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\User\\miniconda3\\envs\\myenv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`. (Deprecated NumPy 1.24)\n", + "DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`. (Deprecated NumPy 1.24)\n" + ] + } + ], + "source": [ + "import gradio as gr\n", + "import pandas as pd\n", + "import hopsworks\n", + "import numpy as np \n", + "from dotenv import load_dotenv\n", + "import os\n", + "from tensorflow.keras.models import load_model\n", + "import datetime as dt\n", + "import mplfinance as mpf \n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connected. Call `.close()` to terminate connection gracefully.\n", + "\n", + "Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/308374\n", + "Connected. Call `.close()` to terminate connection gracefully.\n", + "Connected. Call `.close()` to terminate connection gracefully.\n", + "Downloading model artifact (0 dirs, 1 files)... DONE\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DeprecationWarning: non-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent version\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model downloaded\n" + ] + } + ], + "source": [ + "# download model\n", + "load_dotenv()\n", + "api_key = os.getenv(\"HOPSWORKS_API_KEY\")\n", + "project = hopsworks.login(api_key_value=api_key, project=\"project0\")\n", + "fs = project.get_feature_store()\n", + "\n", + "mr = project.get_model_registry()\n", + "model = mr.get_model(\"btc_model\", version=1)\n", + "model_dir = model.download()\n", + "# Load the model\n", + "model = load_model(model_dir + '/btc_model.h5')\n", + "print(\"Model downloaded\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "UserWarning: Could not establish connection to ArrowFlight Server. (Flight returned timeout error, with message: Deadline Exceeded) Will fall back to hive/spark for this session. If the error persists, you can disable using ArrowFlight by changing the cluster configuration (set 'enable_flyingduck'='false').\n", + "DeprecationWarning: ssl.PROTOCOL_TLS is deprecated\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finished: Reading data from Hopsworks, using Hive (3.88s) \n" + ] + }, + { + "data": { + "text/html": [ + "
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2202 rows × 6 columns

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" + ], + "text/plain": [ + " close_time open high low close \\\n", + "open_time \n", + "2018-01-01 2018-01-01 23:59:59.999 13715.65 13818.55 12750.00 13380.00 \n", + "2018-01-02 2018-01-02 23:59:59.999 13382.16 15473.49 12890.02 14675.11 \n", + "2018-01-03 2018-01-03 23:59:59.999 14690.00 15307.56 14150.00 14919.51 \n", + "2018-01-04 2018-01-04 23:59:59.999 14919.51 15280.00 13918.04 15059.54 \n", + "2018-01-05 2018-01-05 23:59:59.999 15059.56 17176.24 14600.00 16960.39 \n", + "... ... ... ... ... ... \n", + "2024-01-07 2024-01-07 23:59:59.999 43968.32 44480.59 43572.09 43929.02 \n", + "2024-01-08 2024-01-08 23:59:59.999 43929.01 47248.99 43175.00 46951.04 \n", + "2024-01-09 2024-01-09 23:59:59.999 46951.04 47972.00 44748.67 46110.00 \n", + "2024-01-10 2024-01-10 23:59:59.999 46110.00 46666.00 44300.36 45610.52 \n", + "2024-01-11 2024-01-11 23:59:59.999 46654.00 48969.48 45618.10 46811.99 \n", + "\n", + " volume \n", + "open_time \n", + "2018-01-01 8609.915844 \n", + "2018-01-02 20078.092111 \n", + "2018-01-03 15905.667639 \n", + "2018-01-04 21329.649574 \n", + "2018-01-05 23251.491125 \n", + "... ... \n", + "2024-01-07 23023.850800 \n", + "2024-01-08 72814.575890 \n", + "2024-01-09 69927.666170 \n", + "2024-01-10 59693.216970 \n", + "2024-01-11 59523.037270 \n", + "\n", + "[2202 rows x 6 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get last 365 days to use in prediction\n", + "btc_fg = fs.get_feature_group(name=\"btc\", version=1)\n", + "query = btc_fg.select_all()\n", + "feature_join = btc_fg.select([\"open_time\", \"close_time\", 'open', 'high', 'low', 'close', 'volume'])\n", + "df = feature_join.read(online=False, dataframe_type=\"default\", read_options={})\n", + "df_sorted = df.sort_values(by='open_time')\n", + "df = df_sorted.reset_index(drop=True)\n", + "historical_df = df.set_index(\"open_time\")\n", + "historical_df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_min, data_max = historical_df[\"close\"].min(), historical_df[\"close\"].max()\n", + "data = historical_df.iloc[-100:]\n", + "data_dates, data = data[\"close_time\"].values, data[\"close\"].values.reshape(-1, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def scale(data):\n", + " return (data - data_min) / (data_max - data_min) * 2 - 1\n", + "\n", + "def unscale(data):\n", + " return (data + 1) / 2 * (data_max - data_min) + data_min" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "def predict_ahead(data, n_ahead):\n", + " # X.shape = (time_axis, input_size)\n", + " _X = scale(np.copy(data))\n", + " _y = None\n", + "\n", + " if len(_X.shape) == 2:\n", + " _X = _X[np.newaxis, ...]\n", + " for _ in range(n_ahead):\n", + " y = model.predict(_X)[np.newaxis, ...]\n", + " if _y is None:\n", + " _y = y\n", + " else:\n", + " _y = np.concatenate((_y, y), axis=1)\n", + " _X = np.concatenate((_X, y), axis=1)[:, y.shape[0]:]\n", + " return unscale(_y[..., 0])\n", + "\n", + "def predict_and_plot(n_ahead):\n", + " n_ahead = int(n_ahead)\n", + " pred = predict_ahead(data, n_ahead).flatten()\n", + " pred = np.insert(pred, 0, data[-1])\n", + " pred_dates = pd.date_range(start=data_dates[-1], periods = n_ahead + 1)\n", + "\n", + " plt.figure(figsize=(20,10))\n", + " # Plot the historical data\n", + " plt.plot(data_dates[-50:], data[-50:], label=\"Historical data\")\n", + " # Plot the predictions\n", + " plt.plot(pred_dates, pred, label=\"Predictions\", color = 'red', linewidth = 3, linestyle = '--')\n", + " plt.legend(loc = \"upper left\", fontsize = 15)\n", + " plt.title(\"BTCUSDT Price Prediction\", fontsize=30)\n", + " plt.xlabel(\"Date (YYYY-MM-DD)\", fontsize=20)\n", + " plt.ylabel(\"Price ($)\", fontsize=20)\n", + "\n", + " ax = plt.gca()\n", + " ax.xaxis.set_major_locator(mdates.DayLocator(interval=5)) # set major ticks to daily interval\n", + " ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) # format the date display\n", + " \n", + " # Rotate and align the tick labels so they look better\n", + " plt.gcf().autofmt_xdate()\n", + "\n", + "\n", + " plt.xticks(fontsize=15)\n", + " plt.yticks(fontsize=15)\n", + " plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n", + "\n", + " plt.savefig('predicted_prices.png', dpi=300)\n", + " #plt.show()\n", + " return 'predicted_prices.png'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 47ms/step\n", + "1/1 [==============================] - 0s 55ms/step\n", + "1/1 [==============================] - 0s 43ms/step\n", + "1/1 [==============================] - 0s 46ms/step\n", + "1/1 [==============================] - 0s 55ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "user_input_n = 5\n", + "a = predict_and_plot(user_input_n)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DeprecationWarning: \n", + " on_event is deprecated, use lifespan event handlers instead.\n", + "\n", + " Read more about it in the\n", + " [FastAPI docs for Lifespan Events](https://fastapi.tiangolo.com/advanced/events/).\n", + " \n", + "DeprecationWarning: \n", + " on_event is deprecated, use lifespan event handlers instead.\n", + "\n", + " Read more about it in the\n", + " [FastAPI docs for Lifespan Events](https://fastapi.tiangolo.com/advanced/events/).\n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on local URL: http://127.0.0.1:7860\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 51ms/step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MatplotlibDeprecationWarning: Auto-close()ing of figures upon backend switching is deprecated since 3.8 and will be removed two minor releases later. To suppress this warning, explicitly call plt.close('all') first.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 58ms/step\n", + "1/1 [==============================] - 0s 56ms/step\n", + "1/1 [==============================] - 0s 62ms/step\n", + "1/1 [==============================] - 0s 53ms/step\n", + "1/1 [==============================] - 0s 56ms/step\n", + "1/1 [==============================] - 0s 53ms/step\n", + "Keyboard interruption in main thread... closing server.\n" + ] + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Gradio interface\n", + "with gr.Blocks() as demo:\n", + " gr.Markdown(\"#
BTC/USDT Price Prediction
\")\n", + " gr.Markdown(\"### Enter how many days ahead you want to see the BTC price predictions.\")\n", + " \n", + " with gr.Row():\n", + " with gr.Column(scale = 0):\n", + " days_ahead = gr.Number(value=1, label=\"Days ahead\")\n", + " predict_button = gr.Button(\"Predict\", scale = 0)\n", + "\n", + " output_image = gr.Image()\n", + "\n", + " # Define the function to be called when the button is pressed\n", + " predict_button.click(predict_and_plot, inputs=[days_ahead], outputs=[output_image])\n", + " \n", + "demo.launch(debug=True)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.13 ('myenv')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "3cf986cef2fa546fe3f032498b7ba99e6f6a2a5fb52d18405c2d075f2b997d72" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/app/app.py b/app/app.py new file mode 100644 index 0000000..fabed45 --- /dev/null +++ b/app/app.py @@ -0,0 +1,110 @@ +import gradio as gr +import pandas as pd +import hopsworks +import numpy as np +from dotenv import load_dotenv +import os +from tensorflow.keras.models import load_model +import matplotlib.pyplot as plt +import matplotlib.dates as mdates + +# download model +load_dotenv() +api_key = os.getenv("HOPSWORKS_API_KEY") +project = hopsworks.login(api_key_value=api_key, project="project0") +fs = project.get_feature_store() + +mr = project.get_model_registry() +model = mr.get_model("btc_model", version=1) +model_dir = model.download() +# Load the model +model = load_model(model_dir + '/btc_model.h5') +print("Model downloaded") + +# get last days to use in prediction +btc_fg = fs.get_feature_group(name="btc", version=1) +query = btc_fg.select_all() +feature_join = btc_fg.select(["open_time", "close_time", 'open', 'high', 'low', 'close', 'volume']) +df = feature_join.read(online=False, dataframe_type="default", read_options={}) +df_sorted = df.sort_values(by='open_time') +df = df_sorted.reset_index(drop=True) +historical_df = df.set_index("open_time") + +data_min, data_max = historical_df["close"].min(), historical_df["close"].max() +# get the last 100 days data +data = historical_df.iloc[-100:] +data_dates, data = data["close_time"].values, data["close"].values.reshape(-1, 1) + + +def scale(data): + return (data - data_min) / (data_max - data_min) * 2 - 1 + +def unscale(data): + return (data + 1) / 2 * (data_max - data_min) + data_min + + +def predict_ahead(data, n_ahead): + # X.shape = (time_axis, input_size) + _X = scale(np.copy(data)) + _y = None + + if len(_X.shape) == 2: + _X = _X[np.newaxis, ...] + for _ in range(n_ahead): + y = model.predict(_X)[np.newaxis, ...] + if _y is None: + _y = y + else: + _y = np.concatenate((_y, y), axis=1) + _X = np.concatenate((_X, y), axis=1)[:, y.shape[0]:] + return unscale(_y[..., 0]) + +def predict_and_plot(n_ahead): + n_ahead = int(n_ahead) + pred = predict_ahead(data, n_ahead).flatten() + pred = np.insert(pred, 0, data[-1]) + pred_dates = pd.date_range(start=data_dates[-1], periods = n_ahead + 1) + + plt.figure(figsize=(20,10)) + # Plot the historical data + plt.plot(data_dates[-50:], data[-50:], label="Historical data") + # Plot the predictions + plt.plot(pred_dates, pred, label="Predictions", color = 'red', linewidth = 3, linestyle = '--') + plt.legend(loc = "upper left", fontsize = 15) + plt.title("BTC/USDT Price Prediction", fontsize=30) + plt.xlabel("Date (YYYY-MM-DD)", fontsize=20) + plt.ylabel("Price ($)", fontsize=20) + + ax = plt.gca() + ax.xaxis.set_major_locator(mdates.DayLocator(interval=5)) # set major ticks to daily interval + ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) # format the date display + + # Rotate and align the tick labels so they look better + plt.gcf().autofmt_xdate() + + plt.xticks(fontsize=15) + plt.yticks(fontsize=15) + plt.grid(True, which='both', linestyle='--', linewidth=0.5) + + plt.savefig('predicted_prices.png', dpi=300) + #plt.show() + return 'predicted_prices.png' + + + +# Gradio interface +with gr.Blocks() as demo: + gr.Markdown("#
BTC/USDT Price Prediction
") + gr.Markdown("### Enter how many days ahead you want to see the BTC price predictions.") + + with gr.Row(): + with gr.Column(scale = 0): + days_ahead = gr.Number(value=1, label="Days ahead") + predict_button = gr.Button("Predict", scale = 0) + + output_image = gr.Image() + + # Define the function to be called when the button is pressed + predict_button.click(predict_and_plot, inputs=[days_ahead], outputs=[output_image]) + +demo.launch(debug=True) diff --git a/app/requirements.txt b/app/requirements.txt new file mode 100644 index 0000000..f2b9fe9 --- /dev/null +++ b/app/requirements.txt @@ -0,0 +1,5 @@ +hopsworks +dotenv +tensorflow +matplotlib +gradio \ No newline at end of file diff --git a/btc_model/btc_model.h5 b/btc_model/btc_model.h5 new file mode 100644 index 0000000000000000000000000000000000000000..c2fcaaa24310cd11d36e14c3a4442a5521e15218 GIT binary patch literal 414024 zcmeFZ2|ShC_b`5xq6n2TR7gllXvEp;IYOlpmF5ycrG!K(i6~?!g)&rBl+r+kaQ1r4 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z?-1uSTfyNyR+u;FBgQkeKvRAc##L=_dj_lik#GKKrybz-3ZD6o-2DWWuA&|u-`)Sp8ec( zK?D9Ex)9NOrgdnua^0l#Bmi`{p*JJXHt(2WC)ejQ{`u literal 0 HcmV?d00001 diff --git a/feature-pipeline-daily.py b/feature-pipeline-daily.py new file mode 100644 index 0000000..cdeab09 --- /dev/null +++ b/feature-pipeline-daily.py @@ -0,0 +1,60 @@ +import configparser +from binance import Client +import pandas as pd +import numpy as np +import datetime +import hopsworks +from dotenv import load_dotenv +import os +#from binance.spot import Spot + +load_dotenv() +api_key = os.getenv("HOPSWORKS_API_KEY") + +project = hopsworks.login(api_key_value=api_key, project="project0") +fs = project.get_feature_store() + +#read configs +config = configparser.ConfigParser() +config_file_path = "C:/Users/User/Desktop/mldl/project/config.ini" # Replace with the actual path to your config.ini file +config.read(config_file_path) +config.read("config.ini") #The Config.ini file we just created + +api_key = config["binance"]["api_key"] +api_secret = config["binance"]["api_secret"] + +client = Client(api_key, api_secret) +#client = Spot() + +# get today's data +today = datetime.datetime.now() + +symbol = 'BTCUSDT' + +todays_kline = client.get_historical_klines(symbol, Client.KLINE_INTERVAL_1DAY, today.strftime("%d %b, %Y")) +#todays_kline = client.klines("BTCUSDT", "1d",limit = 1) + +# necessary arrangements for today's data +todays_df = pd.DataFrame(todays_kline) + +todays_df.columns = ["Open time","Open","High","Low","Close","Volume","Close time","Quote asset volume","Number of trades", + "Taker buy base asset volume","Taker buy quote asset volume","Ignore"] + + +#We need to convert the Time from Object to datetime format +todays_df["Open time"] = pd.to_datetime(todays_df["Open time"]/1000, unit="s") +todays_df["Close time"] = pd.to_datetime(todays_df["Close time"]/1000, unit="s") + +#Covert others to float datatype +numeric_columns = ["Open","High","Low","Close","Volume","Quote asset volume", + "Taker buy base asset volume","Taker buy quote asset volume","Ignore"] +todays_df[numeric_columns] = todays_df[numeric_columns].apply(pd.to_numeric, axis=1) + +todays_df = todays_df.drop('Ignore', axis=1) +todays_df.columns = todays_df.columns.str.lower().str.replace(' ', '_') + +# print(todays_df) + +#Store today's data in hopsworks +btc_fg = fs.get_feature_group(name="btc",version=1) +btc_fg.insert(todays_df) diff --git a/feature-pipeline.ipynb b/feature-pipeline.ipynb new file mode 100644 index 0000000..0f9008f --- /dev/null +++ b/feature-pipeline.ipynb @@ -0,0 +1,341 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Feature pipeline\n", + "- Register the arranged data to hopsworks \n", + "\n", + "#### Training pipeline\n", + "- read it from hopsworks in training pipeline and split into test train\n", + "- train the model\n", + "- evaluate the model\n", + "- register to hopsworks again\n", + "\n", + "#### Daily inference pipeline\n", + "- get last data and put to hopsworks\n", + "\n", + "#### UI\n", + "- get the recent data from hopsworks\n", + "- get user input (how many days the prediction will be)\n", + "- predict and plot \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import configparser\n", + "from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager\n", + "import pandas as pd\n", + "import numpy as np\n", + "import datetime as dt\n", + "import hopsworks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from dotenv import load_dotenv\n", + "import os\n", + "load_dotenv()\n", + "api_key = os.getenv(\"HOPSWORKS_API_KEY\")\n", + "project = hopsworks.login(api_key_value=api_key, project=\"project0\")\n", + "fs = project.get_feature_store()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#read configs\n", + "config = configparser.ConfigParser()\n", + "config_file_path = \"C:/Users/User/Desktop/mldl/project/config.ini\" # Replace with the actual path to your config.ini file\n", + "config.read(config_file_path)\n", + "config.read(\"config.ini\") #The Config.ini file we just created" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "api_key = config[\"binance\"][\"api_key\"]\n", + "api_secret = config[\"binance\"][\"api_secret\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "client = Client(api_key, api_secret)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tickers = client.get_all_tickers()\n", + "tickers #This is used to get the entire tickets available on Binance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Wrap the data into a pandas dataFrame\n", + "binance_df = pd.DataFrame(tickers)\n", + "binance_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let make the Symbol the Index value\n", + "binance_df.set_index(\"symbol\", inplace=True)\n", + "binance_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "float(binance_df.loc[\"BTCUSDT\"][\"price\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "depth = client.get_order_book(symbol=\"BTCUSDT\")\n", + "depth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "depth_df = pd.DataFrame(depth['bids'])\n", + "depth_df.columns = [\"Price\",\"Volume\"]\n", + "depth_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Get historical data**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "historical_data = client.get_historical_klines(\"BTCUSDT\", Client.KLINE_INTERVAL_1DAY, \"1 Jan 2018\")\n", + "historical_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "historical_df = pd.DataFrame(historical_data)\n", + "\n", + "\n", + "historical_df.columns = [\"Open time\",\"Open\",\"High\",\"Low\",\"Close\",\"Volume\",\"Close time\",\"Quote asset volume\",\"Number of trades\",\n", + " \"Taker buy base asset volume\",\"Taker buy quote asset volume\",\"Ignore\"]\n", + " \n", + "historical_df.head() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "historical_df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Data Transformation & Preprocessing**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#We need to convert the Time from Object to datetime format\n", + "historical_df[\"Open time\"] = pd.to_datetime(historical_df[\"Open time\"]/1000, unit=\"s\")\n", + "historical_df[\"Close time\"] = pd.to_datetime(historical_df[\"Close time\"]/1000, unit=\"s\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Covert others to float datatype\n", + "numeric_columns = [\"Open\",\"High\",\"Low\",\"Close\",\"Volume\",\"Quote asset volume\",\n", + " \"Taker buy base asset volume\",\"Taker buy quote asset volume\",\"Ignore\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "historical_df[numeric_columns] = historical_df[numeric_columns].apply(pd.to_numeric, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "historical_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "historical_df.describe() # This used to get the statistical analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Data Visualization**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import mplfinance as mpf \n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mpf.plot(historical_df.set_index(\"Close time\").tail(200))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mpf.plot(historical_df.set_index(\"Close time\").tail(200),type=\"candle\", style=\"charles\", volume=True,title=\"BTCUSDT Last 200 Days\",\n", + " mav=(10,20,30))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Save features to hopsworks**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "historical_df = historical_df.drop('Ignore', axis=1)\n", + "historical_df.columns = historical_df.columns.str.lower().str.replace(' ', '_')\n", + "historical_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "btc_fg = fs.get_or_create_feature_group(\n", + " name=\"btc\",\n", + " version=1,\n", + " primary_key=[\"open\",\"high\",\"low\",\"close\",\"volume\",\"quote_asset_volume\",\n", + " \"taker_buy_base_asset_volume\",\"taker_buy_quote_asset_volume\"], \n", + " description=\"bitcoin historical data since january 2018\")\n", + "btc_fg.insert(historical_df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.13 ('myenv')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + 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+pyproject-toml +python-ffmpeg +binance-connector +pandas +numpy +python-dotenv +hsfs +twofish \ No newline at end of file diff --git a/lambda_daily_hopsworks/src/main.py b/lambda_daily_hopsworks/src/main.py new file mode 100644 index 0000000..b2ec493 --- /dev/null +++ b/lambda_daily_hopsworks/src/main.py @@ -0,0 +1,60 @@ +import configparser +#from binance import Client +import pandas as pd +import numpy as np +import datetime +import hopsworks +from dotenv import load_dotenv +import os +from binance.spot import Spot + +def handler(event,context): + load_dotenv() + api_key = os.getenv("HOPSWORKS_API_KEY") + + project = hopsworks.login(api_key_value=api_key, project="project0") + fs = project.get_feature_store() + + #read configs + # config = configparser.ConfigParser() + # config_file_path = "C:/Users/User/Desktop/mldl/project/config.ini" # Replace with the actual path to your config.ini file + # config.read(config_file_path) + # config.read("config.ini") #The Config.ini file we just created + + #api_key = config["binance"]["api_key"] + #api_secret = config["binance"]["api_secret"] + + #client = Client(api_key, api_secret) + client = Spot() + + # get today's data + today = datetime.datetime.now() + + symbol = 'BTCUSDT' + #todays_kline = client.get_historical_klines(symbol, Client.KLINE_INTERVAL_1DAY, today.strftime("%d %b, %Y")) + todays_kline = client.klines("BTCUSDT", "1d",limit = 1) + + # necessary arrangements for today's data + todays_df = pd.DataFrame(todays_kline) + + todays_df.columns = ["Open time","Open","High","Low","Close","Volume","Close time","Quote asset volume","Number of trades", + "Taker buy base asset volume","Taker buy quote asset volume","Ignore"] + + + #We need to convert the Time from Object to datetime format + todays_df["Open time"] = pd.to_datetime(todays_df["Open time"]/1000, unit="s") + todays_df["Close time"] = pd.to_datetime(todays_df["Close time"]/1000, unit="s") + + #Covert others to float datatype + numeric_columns = ["Open","High","Low","Close","Volume","Quote asset volume", + "Taker buy base asset volume","Taker buy quote asset volume","Ignore"] + todays_df[numeric_columns] = todays_df[numeric_columns].apply(pd.to_numeric, axis=1) + + todays_df = todays_df.drop('Ignore', axis=1) + todays_df.columns = todays_df.columns.str.lower().str.replace(' ', '_') + + + #Store today's data in hopsworks + btc_fg = fs.get_feature_group(name="btc",version=1) + btc_fg.insert(todays_df) + return '200' \ No newline at end of file diff --git a/predicted_prices.png b/predicted_prices.png new file mode 100644 index 0000000000000000000000000000000000000000..4bab67a3c1220257129f32edcf881868074d79e7 GIT binary patch literal 433974 zcmeFai96S6`#!GenVOoGi6pYLh){$?)|!;;g%+|DArV=#tEZ`uC|R;Z$kX@%f9nHulJ16=Qy6E-`9Oz=XIXvbw57)HI$Yv=2*c<_-`iRnUo$f7g*bnTg#gg4Ot&2x@Z zaAKOr#H6gaOUF5~tI_4=JIBVEz7fCmPk&1Fxzly!oUvf)rXNoI*L$-hd-%o>i*O&q zu+zd>?{3BK2@l+RydnJJzL=kcH^;2?UR28WLhOa$rp_hLAD2%~k9xG^_=lkn3uK)eLwd+1E``_QW 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+python-dotenv +pandas +numpy \ No newline at end of file diff --git a/training-pipeline.ipynb b/training-pipeline.ipynb new file mode 100644 index 0000000..5b9e4c0 --- /dev/null +++ b/training-pipeline.ipynb @@ -0,0 +1,950 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\User\\miniconda3\\envs\\myenv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connected. Call `.close()` to terminate connection gracefully.\n", + "\n", + "Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/308374\n", + "Connected. Call `.close()` to terminate connection gracefully.\n" + ] + } + ], + "source": [ + "import hopsworks\n", + "from dotenv import load_dotenv\n", + "import os\n", + "load_dotenv()\n", + "api_key = os.getenv(\"HOPSWORKS_API_KEY\")\n", + "project = hopsworks.login(api_key_value=api_key, project=\"project0\")\n", + "fs = project.get_feature_store()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "btc_fg = fs.get_feature_group(name=\"btc\", version=1)\n", + "query = btc_fg.select_all()\n", + "# feature_view = fs.get_or_create_feature_view(name=\"btc\",\n", + "# version=1,\n", + "# description=\"Read from bitcoin dataset\",\n", + "# labels=[\"close\"],\n", + "# query=query)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_join = btc_fg.select([\"open_time\", \"close_time\", 'open', 'high', 'low', 'close', 'volume'])\n", + "feature_join" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finished: Reading data from Hopsworks, using ArrowFlight (1.12s) \n" + ] + }, + { + "data": { + "text/html": [ + "

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2178 rows × 7 columns

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" + ], + "text/plain": [ + " open_time close_time open \\\n", + "0 2018-01-01 00:00:00+00:00 2018-01-01 23:59:59.999000+00:00 13715.65 \n", + "1 2018-01-02 00:00:00+00:00 2018-01-02 23:59:59.999000+00:00 13382.16 \n", + "2 2018-01-03 00:00:00+00:00 2018-01-03 23:59:59.999000+00:00 14690.00 \n", + "3 2018-01-04 00:00:00+00:00 2018-01-04 23:59:59.999000+00:00 14919.51 \n", + "4 2018-01-05 00:00:00+00:00 2018-01-05 23:59:59.999000+00:00 15059.56 \n", + "... ... ... ... \n", + "2173 2023-12-14 00:00:00+00:00 2023-12-14 23:59:59.999000+00:00 42869.03 \n", + "2174 2023-12-15 00:00:00+00:00 2023-12-15 23:59:59.999000+00:00 43022.26 \n", + "2175 2023-12-16 00:00:00+00:00 2023-12-16 23:59:59.999000+00:00 41940.29 \n", + "2176 2023-12-17 00:00:00+00:00 2023-12-17 23:59:59.999000+00:00 42278.02 \n", + "2177 2023-12-18 00:00:00+00:00 2023-12-18 23:59:59.999000+00:00 41374.64 \n", + "\n", + " high low close volume \n", + "0 13818.55 12750.00 13380.00 8609.915844 \n", + "1 15473.49 12890.02 14675.11 20078.092111 \n", + "2 15307.56 14150.00 14919.51 15905.667639 \n", + "3 15280.00 13918.04 15059.54 21329.649574 \n", + "4 17176.24 14600.00 16960.39 23251.491125 \n", + "... ... ... ... ... \n", + "2173 43420.00 41400.00 43022.26 42047.057090 \n", + "2174 43080.81 41666.00 41940.30 33421.793200 \n", + "2175 42724.43 41605.00 42278.03 24118.857470 \n", + "2176 42424.07 41510.00 41718.89 24880.677920 \n", + "2177 41470.10 40542.93 41327.70 27379.517180 \n", + "\n", + "[2178 rows x 7 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sorted = df.sort_values(by='open_time')\n", + "df = df_sorted.reset_index(drop=True)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#df.to_csv('btc.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Process data and create a predictive model**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`. (Deprecated NumPy 1.24)\n", + "DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`. (Deprecated NumPy 1.24)\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization, LayerNormalization\n", + "from tensorflow.keras.models import Sequential\n", + "import pandas as pd\n", + "import numpy as np\n", + "import datetime as dt\n", + "import mplfinance as mpf \n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([13380. , 14675.11, 14919.51, ..., 42278.03, 41718.89, 41327.7 ])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "historical_df = df.set_index(\"open_time\")\n", + "historical_df = historical_df[\"close\"].values\n", + "historical_df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "n_historical_days = 100\n", + "n_prediction_days = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2077, 100, 1)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X, y = [], []\n", + "for x in range(n_historical_days, len(historical_df) - n_prediction_days):\n", + " X.append(historical_df[x - n_historical_days:x])\n", + " y.append(historical_df[x:x + n_prediction_days])\n", + "\n", + "X, y = np.array(X)[..., np.newaxis], np.array(y)\n", + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.utils import shuffle\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# Normalization (min-max scaling between -1 and 1)\n", + "data_max, data_min = max(X_train.max(), y_train.max()), min(X_train.min(), y_train.min())\n", + "\n", + "def scale(data):\n", + " return (data - data_min) / (data_max - data_min) * 2 - 1\n", + "\n", + "def unscale(data):\n", + " return (data + 1) / 2 * (data_max - data_min) + data_min\n", + "\n", + "X_train, y_train = scale(X_train), scale(y_train)\n", + "X_test, y_test = scale(X_test), scale(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Shuffle\n", + "X_train , y_train = shuffle(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Create the Neural Network**" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "52/52 [==============================] - 7s 55ms/step - loss: 0.0504 - val_loss: 0.0033\n", + "Epoch 2/10\n", + "52/52 [==============================] - 2s 42ms/step - loss: 0.0104 - val_loss: 0.0022\n", + "Epoch 3/10\n", + "52/52 [==============================] - 2s 45ms/step - loss: 0.0096 - val_loss: 0.0020\n", + "Epoch 4/10\n", + "52/52 [==============================] - 3s 59ms/step - loss: 0.0089 - val_loss: 0.0021\n", + "Epoch 5/10\n", + "52/52 [==============================] - 3s 59ms/step - loss: 0.0088 - val_loss: 0.0018\n", + "Epoch 6/10\n", + "52/52 [==============================] - 3s 57ms/step - loss: 0.0080 - val_loss: 0.0017\n", + "Epoch 7/10\n", + "52/52 [==============================] - 3s 57ms/step - loss: 0.0071 - val_loss: 0.0016\n", + "Epoch 8/10\n", + "52/52 [==============================] - 3s 61ms/step - loss: 0.0077 - val_loss: 0.0017\n", + "Epoch 9/10\n", + "52/52 [==============================] - 3s 60ms/step - loss: 0.0067 - val_loss: 0.0016\n", + "Epoch 10/10\n", + "52/52 [==============================] - 3s 54ms/step - loss: 0.0068 - val_loss: 0.0015\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = Sequential()\n", + "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1:])))\n", + "model.add(Dropout(0.2))\n", + "model.add(LSTM(units=50, return_sequences=False))\n", + "model.add(Dropout(0.2))\n", + "model.add(Dense(units=1))\n", + "\n", + "model.compile(optimizer='adam', loss='mean_squared_error')\n", + "model.fit(\n", + " X_train,\n", + " y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_data=(X_test, y_test)\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_7\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " lstm_12 (LSTM) (None, 100, 50) 10400 \n", + " \n", + " dropout_6 (Dropout) (None, 100, 50) 0 \n", + " \n", + " lstm_13 (LSTM) (None, 50) 20200 \n", + " \n", + " dropout_7 (Dropout) (None, 50) 0 \n", + " \n", + " dense_6 (Dense) (None, 1) 51 \n", + " \n", + "=================================================================\n", + "Total params: 30,651\n", + "Trainable params: 30,651\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Test the Model**" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "actual_prices_scaled = y_test[:, -1]\n", + "# actual_prices_scaled" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 [==============================] - 1s 15ms/step\n" + ] + } + ], + "source": [ + "prediction_prices_scaled = model.predict(X_test)[:, -1]\n", + "# prediction_prices_scaled" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# Perform the inverse scaling.\n", + "prediction_prices = unscale(prediction_prices_scaled)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "actual_prices = unscale(actual_prices_scaled)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Plot model**" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20, 5))\n", + "plt.plot(actual_prices, color=\"black\", label=\"Actual Prices\")\n", + "plt.plot(prediction_prices , color=\"green\", label=\"Predicted prices\")\n", + "plt.title(\"ETHBTC Price Prediction\")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Price\")\n", + "plt.legend(loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1258.0530089966023" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "mse = mean_squared_error(actual_prices, prediction_prices)\n", + "np.sqrt(mse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Delete model**" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# mr = project.get_model_registry()\n", + "# model_old = mr.get_model(\"btc_model\", version=1)\n", + "# model_old.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Register the model**" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connected. Call `.close()` to terminate connection gracefully.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Uploading: 100.000%|██████████| 414024/414024 elapsed<00:02 remaining<00:00 2.44it/s]\n", + "Model export complete: 100%|██████████| 6/6 [00:08<00:00, 1.46s/it] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model created, explore it at https://c.app.hopsworks.ai:443/p/308374/models/btc_model/1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "Model(name: 'btc_model', version: 1)" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mr = project.get_model_registry()\n", + "model_dir=\"btc_model\"\n", + "if os.path.isdir(model_dir) == False:\n", + " os.mkdir(model_dir)\n", + "\n", + "# Save the model\n", + "model.save(model_dir + '/btc_model.h5') # creates a HDF5 file 'btc_model.h5'\n", + "\n", + "btc_model = mr.python.create_model(\n", + " name=\"btc_model\",\n", + " metrics = {\"MSE\": mse},\n", + " description=\"Bitcoin/USD value predictor\"\n", + ")\n", + "\n", + "btc_model.save(model_dir)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.13 ('myenv')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "3cf986cef2fa546fe3f032498b7ba99e6f6a2a5fb52d18405c2d075f2b997d72" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}