Merge branch 'main' into fix-FromAsCasing-warning
This commit is contained in:
commit
79d23dd10a
7
.env.example
Normal file
7
.env.example
Normal file
@ -0,0 +1,7 @@
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|||||||
|
TOKEN=
|
||||||
|
TRAINING_DAYS=
|
||||||
|
TIMEFRAME=
|
||||||
|
MODEL=
|
||||||
|
REGION=
|
||||||
|
DATA_PROVIDER=
|
||||||
|
CG_API_KEY=
|
13
.gitignore
vendored
13
.gitignore
vendored
@ -9,6 +9,13 @@ inference-data
|
|||||||
worker-data
|
worker-data
|
||||||
|
|
||||||
config.json
|
config.json
|
||||||
env
|
/data
|
||||||
env_file
|
|
||||||
.env
|
**/*.venv*
|
||||||
|
**/.cache
|
||||||
|
**/.env
|
||||||
|
**/env_file
|
||||||
|
**/.gitkeep*
|
||||||
|
**/*.csv
|
||||||
|
**/*.pkl
|
||||||
|
**/*.zip
|
||||||
|
@ -1,5 +1,7 @@
|
|||||||
# Use an official Python runtime as the base image
|
FROM python:3.11-slim AS project_env
|
||||||
FROM amd64/python:3.9-buster AS project_env
|
|
||||||
|
# Install curl
|
||||||
|
RUN apt-get update && apt-get install -y curl
|
||||||
|
|
||||||
# Set the working directory in the container
|
# Set the working directory in the container
|
||||||
WORKDIR /app
|
WORKDIR /app
|
||||||
|
45
README.md
45
README.md
@ -1,12 +1,12 @@
|
|||||||
# Basic ETH Price Prediction Node
|
# Basic Price Prediction Node
|
||||||
|
|
||||||
This repository provides an example Allora network worker node, designed to offer price predictions for ETH. The primary objective is to demonstrate the use of a basic inference model running within a dedicated container, showcasing its integration with the Allora network infrastructure to contribute valuable inferences.
|
This repository provides an example [Allora network](https://docs.allora.network/) worker node, designed to offer price predictions. The primary objective is to demonstrate the use of a basic inference model running within a dedicated container, showcasing its integration with the Allora network infrastructure to contribute valuable inferences.
|
||||||
|
|
||||||
## Components
|
## Components
|
||||||
|
|
||||||
- **Worker**: The node that publishes inferences to the Allora chain.
|
- **Worker**: The node that publishes inferences to the Allora chain.
|
||||||
- **Inference**: A container that conducts inferences, maintains the model state, and responds to internal inference requests via a Flask application. This node operates with a basic linear regression model for price predictions.
|
- **Inference**: A container that conducts inferences, maintains the model state, and responds to internal inference requests via a Flask application. This node operates with a basic linear regression model for price predictions.
|
||||||
- **Updater**: A cron-like container designed to update the inference node's data by daily fetching the latest market information from Binance, ensuring the model stays current with new market trends.
|
- **Updater**: A cron-like container designed to update the inference node's data by daily fetching the latest market information from the data provider, ensuring the model stays current with new market trends.
|
||||||
|
|
||||||
Check the `docker-compose.yml` file for the detailed setup of each component.
|
Check the `docker-compose.yml` file for the detailed setup of each component.
|
||||||
|
|
||||||
@ -17,14 +17,45 @@ A complete working example is provided in the `docker-compose.yml` file.
|
|||||||
### Steps to Setup
|
### Steps to Setup
|
||||||
|
|
||||||
1. **Clone the Repository**
|
1. **Clone the Repository**
|
||||||
2. **Copy and Populate Configuration**
|
2. **Copy and Populate Model Configuration environment file**
|
||||||
|
|
||||||
|
Copy the example .env.example file and populate it with your variables:
|
||||||
|
```sh
|
||||||
|
cp .env.example .env
|
||||||
|
```
|
||||||
|
|
||||||
|
Here are the currently accepted configurations:
|
||||||
|
- TOKEN
|
||||||
|
Must be one in ('ETH','SOL','BTC','BNB','ARB').
|
||||||
|
Note: if you are using `Binance` as the data provider, any token could be used.
|
||||||
|
If you are using Coingecko, you should add its `coin_id` in the [token_map here](https://github.com/allora-network/basic-coin-prediction-node/blob/main/updater.py#L107). Find [more info here](https://docs.coingecko.com/reference/simple-price) and the [list here](https://docs.google.com/spreadsheets/d/1wTTuxXt8n9q7C4NDXqQpI3wpKu1_5bGVmP9Xz0XGSyU/edit?usp=sharing).
|
||||||
|
- TRAINING_DAYS
|
||||||
|
Must be an `int` >= 1.
|
||||||
|
Represents how many days of historical data to use.
|
||||||
|
- TIMEFRAME
|
||||||
|
This should be in this form: `10min`, `1h`, `1d`, `1m`, etc.
|
||||||
|
Note: For Coingecko, Data granularity (candle's body) is automatic - [see here](https://docs.coingecko.com/reference/coins-id-ohlc). To avoid downsampling, it is recommanded to use with Coingecko:
|
||||||
|
- TIMEFRAME >= 30m if TRAINING_DAYS <= 2
|
||||||
|
- TIMEFRAME >= 4h if TRAINING_DAYS <= 30
|
||||||
|
- TIMEFRAME >= 4d if TRAINING_DAYS >= 31
|
||||||
|
- MODEL
|
||||||
|
Must be one in ('LinearRegression','SVR','KernelRidge','BayesianRidge').
|
||||||
|
You can easily add support for any other models by [adding it here](https://github.com/allora-network/basic-coin-prediction-node/blob/main/model.py#L133).
|
||||||
|
- REGION
|
||||||
|
Used for the Binance API. This should be in this form: `US`, `EU`, etc.
|
||||||
|
- DATA_PROVIDER
|
||||||
|
Must be `binance` or `coingecko`. Feel free to add support for other data providers to personalize your model!
|
||||||
|
- CG_API_KEY
|
||||||
|
This is your `Coingecko` API key, if you've set `DATA_PROVIDER=coingecko`.
|
||||||
|
|
||||||
|
3. **Copy and Populate Worker Configuration**
|
||||||
|
|
||||||
Copy the example configuration file and populate it with your variables:
|
Copy the example configuration file and populate it with your variables:
|
||||||
```sh
|
```sh
|
||||||
cp config.example.json config.json
|
cp config.example.json config.json
|
||||||
```
|
```
|
||||||
|
|
||||||
3. **Initialize Worker**
|
4. **Initialize Worker**
|
||||||
|
|
||||||
Run the following commands from the project's root directory to initialize the worker:
|
Run the following commands from the project's root directory to initialize the worker:
|
||||||
```sh
|
```sh
|
||||||
@ -35,11 +66,11 @@ A complete working example is provided in the `docker-compose.yml` file.
|
|||||||
- Automatically create Allora keys for your worker.
|
- Automatically create Allora keys for your worker.
|
||||||
- Export the needed variables from the created account to be used by the worker node, bundle them with your provided `config.json`, and pass them to the node as environment variables.
|
- Export the needed variables from the created account to be used by the worker node, bundle them with your provided `config.json`, and pass them to the node as environment variables.
|
||||||
|
|
||||||
4. **Faucet Your Worker Node**
|
5. **Faucet Your Worker Node**
|
||||||
|
|
||||||
You can find the offchain worker node's address in `./worker-data/env_file` under `ALLORA_OFFCHAIN_ACCOUNT_ADDRESS`. [Add faucet funds](https://docs.allora.network/devs/get-started/setup-wallet#add-faucet-funds) to your worker's wallet before starting it.
|
You can find the offchain worker node's address in `./worker-data/env_file` under `ALLORA_OFFCHAIN_ACCOUNT_ADDRESS`. [Add faucet funds](https://docs.allora.network/devs/get-started/setup-wallet#add-faucet-funds) to your worker's wallet before starting it.
|
||||||
|
|
||||||
5. **Start the Services**
|
6. **Start the Services**
|
||||||
|
|
||||||
Run the following command to start the worker node, inference, and updater nodes:
|
Run the following command to start the worker node, inference, and updater nodes:
|
||||||
```sh
|
```sh
|
||||||
|
33
app.py
33
app.py
@ -1,43 +1,28 @@
|
|||||||
import json
|
import json
|
||||||
import pickle
|
from flask import Flask, Response
|
||||||
import pandas as pd
|
from model import download_data, format_data, train_model, get_inference
|
||||||
import numpy as np
|
from config import model_file_path, TOKEN, TIMEFRAME, TRAINING_DAYS, REGION, DATA_PROVIDER
|
||||||
from datetime import datetime
|
|
||||||
from flask import Flask, jsonify, Response
|
|
||||||
from model import download_data, format_data, train_model
|
|
||||||
from config import model_file_path
|
|
||||||
|
|
||||||
app = Flask(__name__)
|
app = Flask(__name__)
|
||||||
|
|
||||||
|
|
||||||
def update_data():
|
def update_data():
|
||||||
"""Download price data, format data and train model."""
|
"""Download price data, format data and train model."""
|
||||||
download_data()
|
files = download_data(TOKEN, TRAINING_DAYS, REGION, DATA_PROVIDER)
|
||||||
format_data()
|
format_data(files, DATA_PROVIDER)
|
||||||
train_model()
|
train_model(TIMEFRAME)
|
||||||
|
|
||||||
|
|
||||||
def get_eth_inference():
|
|
||||||
"""Load model and predict current price."""
|
|
||||||
with open(model_file_path, "rb") as f:
|
|
||||||
loaded_model = pickle.load(f)
|
|
||||||
|
|
||||||
now_timestamp = pd.Timestamp(datetime.now()).timestamp()
|
|
||||||
X_new = np.array([now_timestamp]).reshape(-1, 1)
|
|
||||||
current_price_pred = loaded_model.predict(X_new)
|
|
||||||
|
|
||||||
return current_price_pred[0][0]
|
|
||||||
|
|
||||||
|
|
||||||
@app.route("/inference/<string:token>")
|
@app.route("/inference/<string:token>")
|
||||||
def generate_inference(token):
|
def generate_inference(token):
|
||||||
"""Generate inference for given token."""
|
"""Generate inference for given token."""
|
||||||
if not token or token != "ETH":
|
if not token or token.upper() != TOKEN:
|
||||||
error_msg = "Token is required" if not token else "Token not supported"
|
error_msg = "Token is required" if not token else "Token not supported"
|
||||||
return Response(json.dumps({"error": error_msg}), status=400, mimetype='application/json')
|
return Response(json.dumps({"error": error_msg}), status=400, mimetype='application/json')
|
||||||
|
|
||||||
try:
|
try:
|
||||||
inference = get_eth_inference()
|
inference = get_inference(token.upper(), TIMEFRAME, REGION, DATA_PROVIDER)
|
||||||
return Response(str(inference), status=200)
|
return Response(str(inference), status=200)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
return Response(json.dumps({"error": str(e)}), status=500, mimetype='application/json')
|
return Response(json.dumps({"error": str(e)}), status=500, mimetype='application/json')
|
||||||
|
@ -3,12 +3,12 @@
|
|||||||
"addressKeyName": "test",
|
"addressKeyName": "test",
|
||||||
"addressRestoreMnemonic": "",
|
"addressRestoreMnemonic": "",
|
||||||
"alloraHomeDir": "",
|
"alloraHomeDir": "",
|
||||||
"gas": "1000000",
|
"gas": "auto",
|
||||||
"gasAdjustment": 1.0,
|
"gasAdjustment": 1.5,
|
||||||
"nodeRpc": "http://localhost:26657",
|
"nodeRpc": "https://allora-rpc.testnet.allora.network",
|
||||||
"maxRetries": 1,
|
"maxRetries": 1,
|
||||||
"delay": 1,
|
"delay": 1,
|
||||||
"submitTx": false
|
"submitTx": true
|
||||||
},
|
},
|
||||||
"worker": [
|
"worker": [
|
||||||
{
|
{
|
||||||
|
16
config.py
16
config.py
@ -1,5 +1,21 @@
|
|||||||
import os
|
import os
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
|
||||||
|
# Load environment variables from .env file
|
||||||
|
load_dotenv()
|
||||||
|
|
||||||
app_base_path = os.getenv("APP_BASE_PATH", default=os.getcwd())
|
app_base_path = os.getenv("APP_BASE_PATH", default=os.getcwd())
|
||||||
data_base_path = os.path.join(app_base_path, "data")
|
data_base_path = os.path.join(app_base_path, "data")
|
||||||
model_file_path = os.path.join(data_base_path, "model.pkl")
|
model_file_path = os.path.join(data_base_path, "model.pkl")
|
||||||
|
|
||||||
|
TOKEN = os.getenv("TOKEN").upper()
|
||||||
|
TRAINING_DAYS = os.getenv("TRAINING_DAYS")
|
||||||
|
TIMEFRAME = os.getenv("TIMEFRAME")
|
||||||
|
MODEL = os.getenv("MODEL")
|
||||||
|
REGION = os.getenv("REGION").lower()
|
||||||
|
if REGION in ["us", "com", "usa"]:
|
||||||
|
REGION = "us"
|
||||||
|
else:
|
||||||
|
REGION = "com"
|
||||||
|
DATA_PROVIDER = os.getenv("DATA_PROVIDER").lower()
|
||||||
|
CG_API_KEY = os.getenv("CG_API_KEY", default=None)
|
||||||
|
@ -1,12 +1,14 @@
|
|||||||
services:
|
services:
|
||||||
inference:
|
inference:
|
||||||
container_name: inference-basic-eth-pred
|
container_name: inference
|
||||||
|
env_file:
|
||||||
|
- .env
|
||||||
build: .
|
build: .
|
||||||
command: python -u /app/app.py
|
command: python -u /app/app.py
|
||||||
ports:
|
ports:
|
||||||
- "8000:8000"
|
- "8000:8000"
|
||||||
healthcheck:
|
healthcheck:
|
||||||
test: ["CMD", "curl", "-f", "http://localhost:8000/inference/ETH"]
|
test: ["CMD", "curl", "-f", "http://inference:8000/inference/${TOKEN}"]
|
||||||
interval: 10s
|
interval: 10s
|
||||||
timeout: 5s
|
timeout: 5s
|
||||||
retries: 12
|
retries: 12
|
||||||
@ -14,7 +16,7 @@ services:
|
|||||||
- ./inference-data:/app/data
|
- ./inference-data:/app/data
|
||||||
|
|
||||||
updater:
|
updater:
|
||||||
container_name: updater-basic-eth-pred
|
container_name: updater
|
||||||
build: .
|
build: .
|
||||||
environment:
|
environment:
|
||||||
- INFERENCE_API_ADDRESS=http://inference:8000
|
- INFERENCE_API_ADDRESS=http://inference:8000
|
||||||
@ -31,7 +33,7 @@ services:
|
|||||||
|
|
||||||
worker:
|
worker:
|
||||||
container_name: worker
|
container_name: worker
|
||||||
image: alloranetwork/allora-offchain-node:latest
|
image: alloranetwork/allora-offchain-node:v0.3.0
|
||||||
volumes:
|
volumes:
|
||||||
- ./worker-data:/data
|
- ./worker-data:/data
|
||||||
depends_on:
|
depends_on:
|
||||||
|
@ -36,7 +36,7 @@ ENV_LOADED=$(grep '^ENV_LOADED=' ./worker-data/env_file | cut -d '=' -f 2)
|
|||||||
if [ "$ENV_LOADED" = "false" ]; then
|
if [ "$ENV_LOADED" = "false" ]; then
|
||||||
json_content=$(cat ./config.json)
|
json_content=$(cat ./config.json)
|
||||||
stringified_json=$(echo "$json_content" | jq -c .)
|
stringified_json=$(echo "$json_content" | jq -c .)
|
||||||
docker run -it --entrypoint=bash -v $(pwd)/worker-data:/data -v $(pwd)/scripts:/scripts -e NAME="${nodeName}" -e ALLORA_OFFCHAIN_NODE_CONFIG_JSON="${stringified_json}" alloranetwork/allora-chain:latest -c "bash /scripts/init.sh"
|
docker run -it --entrypoint=bash -v $(pwd)/worker-data:/data -v $(pwd)/scripts:/scripts -e NAME="${nodeName}" -e ALLORA_OFFCHAIN_NODE_CONFIG_JSON="${stringified_json}" alloranetwork/allora-chain:v0.4.0 -c "bash /scripts/init.sh"
|
||||||
echo "config.json saved to ./worker-data/env_file"
|
echo "config.json saved to ./worker-data/env_file"
|
||||||
else
|
else
|
||||||
echo "config.json is already loaded, skipping the operation. You can set ENV_LOADED variable to false in ./worker-data/env_file to reload the config.json"
|
echo "config.json is already loaded, skipping the operation. You can set ENV_LOADED variable to false in ./worker-data/env_file to reload the config.json"
|
||||||
|
196
model.py
196
model.py
@ -1,98 +1,141 @@
|
|||||||
|
import json
|
||||||
import os
|
import os
|
||||||
import pickle
|
import pickle
|
||||||
from zipfile import ZipFile
|
from zipfile import ZipFile
|
||||||
from datetime import datetime
|
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
from sklearn.model_selection import train_test_split
|
from sklearn.kernel_ridge import KernelRidge
|
||||||
from sklearn.linear_model import LinearRegression
|
from sklearn.linear_model import BayesianRidge, LinearRegression
|
||||||
from updater import download_binance_monthly_data, download_binance_daily_data
|
from sklearn.svm import SVR
|
||||||
from config import data_base_path, model_file_path
|
from updater import download_binance_daily_data, download_binance_current_day_data, download_coingecko_data, download_coingecko_current_day_data
|
||||||
|
from config import data_base_path, model_file_path, TOKEN, MODEL, CG_API_KEY
|
||||||
|
|
||||||
|
|
||||||
binance_data_path = os.path.join(data_base_path, "binance/futures-klines")
|
binance_data_path = os.path.join(data_base_path, "binance")
|
||||||
training_price_data_path = os.path.join(data_base_path, "eth_price_data.csv")
|
coingecko_data_path = os.path.join(data_base_path, "coingecko")
|
||||||
|
training_price_data_path = os.path.join(data_base_path, "price_data.csv")
|
||||||
|
|
||||||
|
|
||||||
def download_data():
|
def download_data_binance(token, training_days, region):
|
||||||
cm_or_um = "um"
|
files = download_binance_daily_data(f"{token}USDT", training_days, region, binance_data_path)
|
||||||
symbols = ["ETHUSDT"]
|
print(f"Downloaded {len(files)} new files")
|
||||||
intervals = ["1d"]
|
return files
|
||||||
years = ["2020", "2021", "2022", "2023", "2024"]
|
|
||||||
months = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
|
def download_data_coingecko(token, training_days):
|
||||||
download_path = binance_data_path
|
files = download_coingecko_data(token, training_days, coingecko_data_path, CG_API_KEY)
|
||||||
download_binance_monthly_data(
|
print(f"Downloaded {len(files)} new files")
|
||||||
cm_or_um, symbols, intervals, years, months, download_path
|
return files
|
||||||
)
|
|
||||||
print(f"Downloaded monthly data to {download_path}.")
|
|
||||||
current_datetime = datetime.now()
|
|
||||||
current_year = current_datetime.year
|
|
||||||
current_month = current_datetime.month
|
|
||||||
download_binance_daily_data(
|
|
||||||
cm_or_um, symbols, intervals, current_year, current_month, download_path
|
|
||||||
)
|
|
||||||
print(f"Downloaded daily data to {download_path}.")
|
|
||||||
|
|
||||||
|
|
||||||
def format_data():
|
def download_data(token, training_days, region, data_provider):
|
||||||
files = sorted([x for x in os.listdir(binance_data_path)])
|
if data_provider == "coingecko":
|
||||||
|
return download_data_coingecko(token, int(training_days))
|
||||||
|
elif data_provider == "binance":
|
||||||
|
return download_data_binance(token, training_days, region)
|
||||||
|
else:
|
||||||
|
raise ValueError("Unsupported data provider")
|
||||||
|
|
||||||
|
def format_data(files, data_provider):
|
||||||
|
if not files:
|
||||||
|
print("Already up to date")
|
||||||
|
return
|
||||||
|
|
||||||
|
if data_provider == "binance":
|
||||||
|
files = sorted([x for x in os.listdir(binance_data_path) if x.startswith(f"{TOKEN}USDT")])
|
||||||
|
elif data_provider == "coingecko":
|
||||||
|
files = sorted([x for x in os.listdir(coingecko_data_path) if x.endswith(".json")])
|
||||||
|
|
||||||
# No files to process
|
# No files to process
|
||||||
if len(files) == 0:
|
if len(files) == 0:
|
||||||
return
|
return
|
||||||
|
|
||||||
price_df = pd.DataFrame()
|
price_df = pd.DataFrame()
|
||||||
for file in files:
|
if data_provider == "binance":
|
||||||
zip_file_path = os.path.join(binance_data_path, file)
|
for file in files:
|
||||||
|
zip_file_path = os.path.join(binance_data_path, file)
|
||||||
|
|
||||||
if not zip_file_path.endswith(".zip"):
|
if not zip_file_path.endswith(".zip"):
|
||||||
continue
|
continue
|
||||||
|
|
||||||
myzip = ZipFile(zip_file_path)
|
myzip = ZipFile(zip_file_path)
|
||||||
with myzip.open(myzip.filelist[0]) as f:
|
with myzip.open(myzip.filelist[0]) as f:
|
||||||
line = f.readline()
|
line = f.readline()
|
||||||
header = 0 if line.decode("utf-8").startswith("open_time") else None
|
header = 0 if line.decode("utf-8").startswith("open_time") else None
|
||||||
df = pd.read_csv(myzip.open(myzip.filelist[0]), header=header).iloc[:, :11]
|
df = pd.read_csv(myzip.open(myzip.filelist[0]), header=header).iloc[:, :11]
|
||||||
df.columns = [
|
df.columns = [
|
||||||
"start_time",
|
"start_time",
|
||||||
"open",
|
"open",
|
||||||
"high",
|
"high",
|
||||||
"low",
|
"low",
|
||||||
"close",
|
"close",
|
||||||
"volume",
|
"volume",
|
||||||
"end_time",
|
"end_time",
|
||||||
"volume_usd",
|
"volume_usd",
|
||||||
"n_trades",
|
"n_trades",
|
||||||
"taker_volume",
|
"taker_volume",
|
||||||
"taker_volume_usd",
|
"taker_volume_usd",
|
||||||
]
|
]
|
||||||
df.index = [pd.Timestamp(x + 1, unit="ms") for x in df["end_time"]]
|
df.index = [pd.Timestamp(x + 1, unit="ms").to_datetime64() for x in df["end_time"]]
|
||||||
df.index.name = "date"
|
df.index.name = "date"
|
||||||
price_df = pd.concat([price_df, df])
|
price_df = pd.concat([price_df, df])
|
||||||
|
|
||||||
price_df.sort_index().to_csv(training_price_data_path)
|
price_df.sort_index().to_csv(training_price_data_path)
|
||||||
|
elif data_provider == "coingecko":
|
||||||
|
for file in files:
|
||||||
|
with open(os.path.join(coingecko_data_path, file), "r") as f:
|
||||||
|
data = json.load(f)
|
||||||
|
df = pd.DataFrame(data)
|
||||||
|
df.columns = [
|
||||||
|
"timestamp",
|
||||||
|
"open",
|
||||||
|
"high",
|
||||||
|
"low",
|
||||||
|
"close"
|
||||||
|
]
|
||||||
|
df["date"] = pd.to_datetime(df["timestamp"], unit="ms")
|
||||||
|
df.drop(columns=["timestamp"], inplace=True)
|
||||||
|
df.set_index("date", inplace=True)
|
||||||
|
price_df = pd.concat([price_df, df])
|
||||||
|
|
||||||
|
price_df.sort_index().to_csv(training_price_data_path)
|
||||||
|
|
||||||
|
|
||||||
def train_model():
|
def load_frame(frame, timeframe):
|
||||||
# Load the eth price data
|
print(f"Loading data...")
|
||||||
|
df = frame.loc[:,['open','high','low','close']].dropna()
|
||||||
|
df[['open','high','low','close']] = df[['open','high','low','close']].apply(pd.to_numeric)
|
||||||
|
df['date'] = frame['date'].apply(pd.to_datetime)
|
||||||
|
df.set_index('date', inplace=True)
|
||||||
|
df.sort_index(inplace=True)
|
||||||
|
|
||||||
|
return df.resample(f'{timeframe}', label='right', closed='right', origin='end').mean()
|
||||||
|
|
||||||
|
def train_model(timeframe):
|
||||||
|
# Load the price data
|
||||||
price_data = pd.read_csv(training_price_data_path)
|
price_data = pd.read_csv(training_price_data_path)
|
||||||
df = pd.DataFrame()
|
df = load_frame(price_data, timeframe)
|
||||||
|
|
||||||
# Convert 'date' to a numerical value (timestamp) we can use for regression
|
print(df.tail())
|
||||||
df["date"] = pd.to_datetime(price_data["date"])
|
|
||||||
df["date"] = df["date"].map(pd.Timestamp.timestamp)
|
|
||||||
|
|
||||||
df["price"] = price_data[["open", "close", "high", "low"]].mean(axis=1)
|
y_train = df['close'].shift(-1).dropna().values
|
||||||
|
X_train = df[:-1]
|
||||||
|
|
||||||
# Reshape the data to the shape expected by sklearn
|
print(f"Training data shape: {X_train.shape}, {y_train.shape}")
|
||||||
x = df["date"].values.reshape(-1, 1)
|
|
||||||
y = df["price"].values.reshape(-1, 1)
|
|
||||||
|
|
||||||
# Split the data into training set and test set
|
# Define the model
|
||||||
x_train, _, y_train, _ = train_test_split(x, y, test_size=0.2, random_state=0)
|
if MODEL == "LinearRegression":
|
||||||
|
model = LinearRegression()
|
||||||
|
elif MODEL == "SVR":
|
||||||
|
model = SVR()
|
||||||
|
elif MODEL == "KernelRidge":
|
||||||
|
model = KernelRidge()
|
||||||
|
elif MODEL == "BayesianRidge":
|
||||||
|
model = BayesianRidge()
|
||||||
|
# Add more models here
|
||||||
|
else:
|
||||||
|
raise ValueError("Unsupported model")
|
||||||
|
|
||||||
# Train the model
|
# Train the model
|
||||||
model = LinearRegression()
|
model.fit(X_train, y_train)
|
||||||
model.fit(x_train, y_train)
|
|
||||||
|
|
||||||
# create the model's parent directory if it doesn't exist
|
# create the model's parent directory if it doesn't exist
|
||||||
os.makedirs(os.path.dirname(model_file_path), exist_ok=True)
|
os.makedirs(os.path.dirname(model_file_path), exist_ok=True)
|
||||||
@ -102,3 +145,22 @@ def train_model():
|
|||||||
pickle.dump(model, f)
|
pickle.dump(model, f)
|
||||||
|
|
||||||
print(f"Trained model saved to {model_file_path}")
|
print(f"Trained model saved to {model_file_path}")
|
||||||
|
|
||||||
|
|
||||||
|
def get_inference(token, timeframe, region, data_provider):
|
||||||
|
"""Load model and predict current price."""
|
||||||
|
with open(model_file_path, "rb") as f:
|
||||||
|
loaded_model = pickle.load(f)
|
||||||
|
|
||||||
|
# Get current price
|
||||||
|
if data_provider == "coingecko":
|
||||||
|
X_new = load_frame(download_coingecko_current_day_data(token, CG_API_KEY), timeframe)
|
||||||
|
else:
|
||||||
|
X_new = load_frame(download_binance_current_day_data(f"{TOKEN}USDT", region), timeframe)
|
||||||
|
|
||||||
|
print(X_new.tail())
|
||||||
|
print(X_new.shape)
|
||||||
|
|
||||||
|
current_price_pred = loaded_model.predict(X_new)
|
||||||
|
|
||||||
|
return current_price_pred[0]
|
@ -1,7 +1,9 @@
|
|||||||
flask[async]
|
flask[async]
|
||||||
gunicorn[gthread]
|
gunicorn[gthread]
|
||||||
numpy==1.26.2
|
numpy
|
||||||
pandas==2.1.3
|
pandas
|
||||||
Requests==2.32.0
|
Requests
|
||||||
scikit_learn==1.3.2
|
aiohttp
|
||||||
werkzeug>=3.0.3 # not directly required, pinned by Snyk to avoid a vulnerability
|
multiprocess
|
||||||
|
scikit_learn
|
||||||
|
python-dotenv
|
204
updater.py
204
updater.py
@ -1,59 +1,175 @@
|
|||||||
import os
|
import os
|
||||||
|
from datetime import date, timedelta
|
||||||
|
import pathlib
|
||||||
|
import time
|
||||||
import requests
|
import requests
|
||||||
|
from requests.adapters import HTTPAdapter
|
||||||
|
from urllib3.util import Retry
|
||||||
from concurrent.futures import ThreadPoolExecutor
|
from concurrent.futures import ThreadPoolExecutor
|
||||||
|
import pandas as pd
|
||||||
|
import json
|
||||||
|
|
||||||
|
|
||||||
|
# Define the retry strategy
|
||||||
|
retry_strategy = Retry(
|
||||||
|
total=4, # Maximum number of retries
|
||||||
|
backoff_factor=2, # Exponential backoff factor (e.g., 2 means 1, 2, 4, 8 seconds, ...)
|
||||||
|
status_forcelist=[429, 500, 502, 503, 504], # HTTP status codes to retry on
|
||||||
|
)
|
||||||
|
|
||||||
|
# Create an HTTP adapter with the retry strategy and mount it to session
|
||||||
|
adapter = HTTPAdapter(max_retries=retry_strategy)
|
||||||
|
|
||||||
|
# Create a new session object
|
||||||
|
session = requests.Session()
|
||||||
|
session.mount('http://', adapter)
|
||||||
|
session.mount('https://', adapter)
|
||||||
|
|
||||||
|
|
||||||
|
files = []
|
||||||
|
|
||||||
|
|
||||||
# Function to download the URL, called asynchronously by several child processes
|
# Function to download the URL, called asynchronously by several child processes
|
||||||
def download_url(url, download_path):
|
def download_url(url, download_path, name=None):
|
||||||
target_file_path = os.path.join(download_path, os.path.basename(url))
|
try:
|
||||||
if os.path.exists(target_file_path):
|
global files
|
||||||
# print(f"File already exists: {url}")
|
if name:
|
||||||
return
|
file_name = os.path.join(download_path, name)
|
||||||
|
else:
|
||||||
|
file_name = os.path.join(download_path, os.path.basename(url))
|
||||||
|
dir_path = os.path.dirname(file_name)
|
||||||
|
pathlib.Path(dir_path).mkdir(parents=True, exist_ok=True)
|
||||||
|
if os.path.isfile(file_name):
|
||||||
|
# print(f"{file_name} already exists")
|
||||||
|
return
|
||||||
|
# Make a request using the session object
|
||||||
|
response = session.get(url)
|
||||||
|
if response.status_code == 404:
|
||||||
|
print(f"File does not exist: {url}")
|
||||||
|
elif response.status_code == 200:
|
||||||
|
with open(file_name, 'wb') as f:
|
||||||
|
f.write(response.content)
|
||||||
|
# print(f"Downloaded: {url} to {file_name}")
|
||||||
|
files.append(file_name)
|
||||||
|
return
|
||||||
|
else:
|
||||||
|
print(f"Failed to download {url}")
|
||||||
|
return
|
||||||
|
except Exception as e:
|
||||||
|
print(str(e))
|
||||||
|
|
||||||
response = requests.get(url)
|
|
||||||
if response.status_code == 404:
|
# Function to generate a range of dates
|
||||||
# print(f"File not exist: {url}")
|
def daterange(start_date, end_date):
|
||||||
pass
|
for n in range(int((end_date - start_date).days)):
|
||||||
|
yield start_date + timedelta(n)
|
||||||
|
|
||||||
|
|
||||||
|
# Function to download daily data from Binance
|
||||||
|
def download_binance_daily_data(pair, training_days, region, download_path):
|
||||||
|
base_url = f"https://data.binance.vision/data/spot/daily/klines"
|
||||||
|
|
||||||
|
end_date = date.today()
|
||||||
|
start_date = end_date - timedelta(days=int(training_days))
|
||||||
|
|
||||||
|
global files
|
||||||
|
files = []
|
||||||
|
|
||||||
|
with ThreadPoolExecutor() as executor:
|
||||||
|
print(f"Downloading data for {pair}")
|
||||||
|
for single_date in daterange(start_date, end_date):
|
||||||
|
url = f"{base_url}/{pair}/1m/{pair}-1m-{single_date}.zip"
|
||||||
|
executor.submit(download_url, url, download_path)
|
||||||
|
|
||||||
|
return files
|
||||||
|
|
||||||
|
|
||||||
|
def download_binance_current_day_data(pair, region):
|
||||||
|
limit = 1000
|
||||||
|
base_url = f'https://api.binance.{region}/api/v3/klines?symbol={pair}&interval=1m&limit={limit}'
|
||||||
|
|
||||||
|
# Make a request using the session object
|
||||||
|
response = session.get(base_url)
|
||||||
|
response.raise_for_status()
|
||||||
|
resp = str(response.content, 'utf-8').rstrip()
|
||||||
|
|
||||||
|
columns = ['start_time','open','high','low','close','volume','end_time','volume_usd','n_trades','taker_volume','taker_volume_usd','ignore']
|
||||||
|
|
||||||
|
df = pd.DataFrame(json.loads(resp),columns=columns)
|
||||||
|
df['date'] = [pd.to_datetime(x+1,unit='ms') for x in df['end_time']]
|
||||||
|
df['date'] = df['date'].apply(pd.to_datetime)
|
||||||
|
df[["volume", "taker_volume", "open", "high", "low", "close"]] = df[["volume", "taker_volume", "open", "high", "low", "close"]].apply(pd.to_numeric)
|
||||||
|
|
||||||
|
return df.sort_index()
|
||||||
|
|
||||||
|
|
||||||
|
def get_coingecko_coin_id(token):
|
||||||
|
token_map = {
|
||||||
|
'ETH': 'ethereum',
|
||||||
|
'SOL': 'solana',
|
||||||
|
'BTC': 'bitcoin',
|
||||||
|
'BNB': 'binancecoin',
|
||||||
|
'ARB': 'arbitrum',
|
||||||
|
# Add more tokens here
|
||||||
|
}
|
||||||
|
|
||||||
|
token = token.upper()
|
||||||
|
if token in token_map:
|
||||||
|
return token_map[token]
|
||||||
else:
|
else:
|
||||||
|
raise ValueError("Unsupported token")
|
||||||
# create the entire path if it doesn't exist
|
|
||||||
os.makedirs(os.path.dirname(target_file_path), exist_ok=True)
|
|
||||||
|
|
||||||
with open(target_file_path, "wb") as f:
|
|
||||||
f.write(response.content)
|
|
||||||
# print(f"Downloaded: {url} to {target_file_path}")
|
|
||||||
|
|
||||||
|
|
||||||
def download_binance_monthly_data(
|
def download_coingecko_data(token, training_days, download_path, CG_API_KEY):
|
||||||
cm_or_um, symbols, intervals, years, months, download_path
|
if training_days <= 7:
|
||||||
):
|
days = 7
|
||||||
# Verify if CM_OR_UM is correct, if not, exit
|
elif training_days <= 14:
|
||||||
if cm_or_um not in ["cm", "um"]:
|
days = 14
|
||||||
print("CM_OR_UM can be only cm or um")
|
elif training_days <= 30:
|
||||||
return
|
days = 30
|
||||||
base_url = f"https://data.binance.vision/data/futures/{cm_or_um}/monthly/klines"
|
elif training_days <= 90:
|
||||||
|
days = 90
|
||||||
|
elif training_days <= 180:
|
||||||
|
days = 180
|
||||||
|
elif training_days <= 365:
|
||||||
|
days = 365
|
||||||
|
else:
|
||||||
|
days = "max"
|
||||||
|
print(f"Days: {days}")
|
||||||
|
|
||||||
# Main loop to iterate over all the arrays and launch child processes
|
coin_id = get_coingecko_coin_id(token)
|
||||||
with ThreadPoolExecutor() as executor:
|
print(f"Coin ID: {coin_id}")
|
||||||
for symbol in symbols:
|
|
||||||
for interval in intervals:
|
|
||||||
for year in years:
|
|
||||||
for month in months:
|
|
||||||
url = f"{base_url}/{symbol}/{interval}/{symbol}-{interval}-{year}-{month}.zip"
|
|
||||||
executor.submit(download_url, url, download_path)
|
|
||||||
|
|
||||||
|
# Get OHLC data from Coingecko
|
||||||
|
url = f'https://api.coingecko.com/api/v3/coins/{coin_id}/ohlc?vs_currency=usd&days={days}&api_key={CG_API_KEY}'
|
||||||
|
|
||||||
def download_binance_daily_data(
|
global files
|
||||||
cm_or_um, symbols, intervals, year, month, download_path
|
files = []
|
||||||
):
|
|
||||||
if cm_or_um not in ["cm", "um"]:
|
|
||||||
print("CM_OR_UM can be only cm or um")
|
|
||||||
return
|
|
||||||
base_url = f"https://data.binance.vision/data/futures/{cm_or_um}/daily/klines"
|
|
||||||
|
|
||||||
with ThreadPoolExecutor() as executor:
|
with ThreadPoolExecutor() as executor:
|
||||||
for symbol in symbols:
|
print(f"Downloading data for {coin_id}")
|
||||||
for interval in intervals:
|
name = os.path.basename(url).split("?")[0].replace("/", "_") + ".json"
|
||||||
for day in range(1, 32): # Assuming days range from 1 to 31
|
executor.submit(download_url, url, download_path, name)
|
||||||
url = f"{base_url}/{symbol}/{interval}/{symbol}-{interval}-{year}-{month:02d}-{day:02d}.zip"
|
|
||||||
executor.submit(download_url, url, download_path)
|
return files
|
||||||
|
|
||||||
|
|
||||||
|
def download_coingecko_current_day_data(token, CG_API_KEY):
|
||||||
|
coin_id = get_coingecko_coin_id(token)
|
||||||
|
print(f"Coin ID: {coin_id}")
|
||||||
|
|
||||||
|
url = f'https://api.coingecko.com/api/v3/coins/{coin_id}/ohlc?vs_currency=usd&days=1&api_key={CG_API_KEY}'
|
||||||
|
|
||||||
|
# Make a request using the session object
|
||||||
|
response = session.get(url)
|
||||||
|
response.raise_for_status()
|
||||||
|
resp = str(response.content, 'utf-8').rstrip()
|
||||||
|
|
||||||
|
columns = ['timestamp','open','high','low','close']
|
||||||
|
|
||||||
|
df = pd.DataFrame(json.loads(resp), columns=columns)
|
||||||
|
df['date'] = [pd.to_datetime(x,unit='ms') for x in df['timestamp']]
|
||||||
|
df['date'] = df['date'].apply(pd.to_datetime)
|
||||||
|
df[["open", "high", "low", "close"]] = df[["open", "high", "low", "close"]].apply(pd.to_numeric)
|
||||||
|
|
||||||
|
return df.sort_index()
|
||||||
|
Loading…
Reference in New Issue
Block a user