init commit
This commit is contained in:
ALIHAN DIKEL
5
.gitignore
vendored
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5
.gitignore
vendored
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@@ -0,0 +1,5 @@
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tmp
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venv
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.idea/
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.ipynb_checkpoints
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*.pkl
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39
README.md
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39
README.md
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@@ -0,0 +1,39 @@
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## Design Decisions
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* Fetching
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* Ticker Symbols
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* dependency to StockSymbol API:
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* https://stock-symbol.herokuapp.com/
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* https://pypi.org/project/stocksymbol/
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* Price Data
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* Yahoo Finance API
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## Code Structure
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```
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|
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|-- assets
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| |-- portfolio.py : dataclasses to represent wealth
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|-- config
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| |-- portfolios.yml : initial states of portfolios
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| |-- strategies.yml : rules and parameters to generate signals
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|-- data
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| |-- YYMMDD : scrapped historical trading data
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|-- ops
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|-- fetch.py : methods to get hist trading data
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|-- transform.py : methods to transform any data
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|- backtest.py : performs backtesting based on specified strategy
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|- preprocess.py : retrieves hist trading data
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```
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## Installation
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### Installing TA-Lib on ARM-based Macs
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```bash
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brew install ta-lib
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export TA_INCLUDE_PATH="$(brew --prefix ta-lib)/include"
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export TA_LIBRARY_PATH="$(brew --prefix ta-lib)/lib"
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pip install ta-lib
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```
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[source for TA-Lib](https://github.com/TA-Lib/ta-lib-python/issues/418#issuecomment-826129619)
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55
backtest.py
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55
backtest.py
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import numpy as np
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from backtesting import Backtest
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from loguru import logger
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from ops.portfolio import PortfolioLoader
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from ops.fetch import DataFetcher
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from ops.strategy import PCTrader
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# prepare historical data
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data_fetcher = DataFetcher()
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_, dfs_ohlc = data_fetcher.load_from_pickle()
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# prepare portfoliio
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portf_loader = PortfolioLoader(path="config/portfolios.yml")
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portfolios = portf_loader.load_from_yaml()
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portfolio = portfolios["backtest_portf"]
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# start backtesting
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final_equities = []
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optims = []
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for stock in portfolio.stocks:
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logger.debug(f"backtesting for: {stock.symbol}.IS")
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data = dfs_ohlc[f"{stock.symbol}.IS"]
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bt = Backtest(data, PCTrader, cash=10000, commission=.002)
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stats = bt.run()
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stats, heatmap = bt.optimize(
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pct=range(1, 5, 1),
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maximize='Equity Final [$]',
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max_tries=200,
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random_state=0,
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return_heatmap=True)
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optims.append((stats, heatmap))
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#bt.plot(filename=f"data/bt_plots/{stock.symbol}")
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#final_equities.append(stats.get('Equity Final [$]'))
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final_equities = np.array(final_equities)
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#print(stats['_trades'].to_string())
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# n_enter=range(15, 35, 5),
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# n_exit=range(10, 25, 5),
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# constraint=lambda p: p.pct,
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"""
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# prepare strategy
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strat_loader = StrategyLoader(path="config/strategies.yml")
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strategy = strat_loader.load_from_yaml(name="PercentChange")
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"""
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# run backtest
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21
config/portfolios.yml
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21
config/portfolios.yml
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# ["KCHOL", "SAHOL", "FROTO", "TOASO", "TCELL", "TTKOM", "THYAO", "PGSUS"]
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portfolios:
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- name: backtest_portf
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stocks:
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- symbol: KCHOL
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- symbol: SAHOL
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- symbol: FROTO
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- symbol: TOASO
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- symbol: TCELL
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- symbol: TTKOM
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- symbol: THYAO
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- symbol: PGSUS
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cash: 10000.0
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# - name: Portfolio2
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# stocks:
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# - symbol: MSFT
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# quantity: 8
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# - symbol: AMZN
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# quantity: 3
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# cash: 7000.0
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4
config/strategies.yml
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4
config/strategies.yml
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strategies:
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- name: PercentChange
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params:
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change_threshold: 0.02
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61
data/bt_plots.html
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61
data/bt_plots.html
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File diff suppressed because one or more lines are too long
61
data/bt_plots/KCHOL.html
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61
data/bt_plots/KCHOL.html
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File diff suppressed because one or more lines are too long
61
data/bt_plots/SAHOL.html
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61
data/bt_plots/SAHOL.html
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File diff suppressed because one or more lines are too long
786
notebooks/dev_backtest.ipynb
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786
notebooks/dev_backtest.ipynb
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File diff suppressed because one or more lines are too long
307
notebooks/dev_trendcuts.ipynb
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307
notebooks/dev_trendcuts.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "2c4f2143-0f9f-47b6-9933-cc3e13347530",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/tcudikel/Dev/sandbox/trade-strat/venv/lib/python3.9/site-packages/urllib3/__init__.py:34: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
|
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"import sys; sys.path.append(\"../\")\n",
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"import os; os.chdir(\"../\")\n",
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"\n",
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"from loguru import logger\n",
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"import pandas as pd\n",
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"import finplot as fplt\n",
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"\n",
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"from ops.portfolio import PortfolioLoader\n",
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"from ops.fetch import DataFetcher\n",
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"\n",
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"\n",
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"# prepare historical data\n",
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"data_fetcher = DataFetcher()\n",
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"_, dfs_ohlc = data_fetcher.load_from_pickle()\n",
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"\n",
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"# prepare portfoliio\n",
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"portf_loader = PortfolioLoader(path=\"config/portfolios.yml\")\n",
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"portfolios = portf_loader.load_from_yaml()\n",
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"portfolio = portfolios[\"backtest_portf\"]"
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]
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 2,
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||||
"id": "980fb1f2-7de9-4913-8943-cd00aa745de4",
|
||||
"metadata": {},
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"outputs": [],
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"source": [
|
||||
"for stock in portfolio.stocks:\n",
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" data = dfs_ohlc[f\"{stock.symbol}.IS\"]\n",
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" break"
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]
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},
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{
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||||
"cell_type": "code",
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||||
"execution_count": 3,
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||||
"id": "af2694bc-2c79-4744-973f-e5e5d4ada0c4",
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||||
"metadata": {},
|
||||
"outputs": [
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||||
{
|
||||
"data": {
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||||
"text/html": [
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||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
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||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
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||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2022-02-07</th>\n",
|
||||
" <td>32.560001</td>\n",
|
||||
" <td>31.100000</td>\n",
|
||||
" <td>32.580002</td>\n",
|
||||
" <td>30.500000</td>\n",
|
||||
" <td>14509607</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2022-02-08</th>\n",
|
||||
" <td>33.320000</td>\n",
|
||||
" <td>32.580002</td>\n",
|
||||
" <td>33.439999</td>\n",
|
||||
" <td>32.500000</td>\n",
|
||||
" <td>15159861</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2022-02-09</th>\n",
|
||||
" <td>33.660000</td>\n",
|
||||
" <td>33.580002</td>\n",
|
||||
" <td>33.880001</td>\n",
|
||||
" <td>33.200001</td>\n",
|
||||
" <td>12581438</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2022-02-10</th>\n",
|
||||
" <td>33.720001</td>\n",
|
||||
" <td>33.720001</td>\n",
|
||||
" <td>34.119999</td>\n",
|
||||
" <td>33.360001</td>\n",
|
||||
" <td>13014121</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2022-02-11</th>\n",
|
||||
" <td>33.799999</td>\n",
|
||||
" <td>33.320000</td>\n",
|
||||
" <td>33.880001</td>\n",
|
||||
" <td>32.439999</td>\n",
|
||||
" <td>17758379</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-01-30</th>\n",
|
||||
" <td>163.199997</td>\n",
|
||||
" <td>162.899994</td>\n",
|
||||
" <td>164.500000</td>\n",
|
||||
" <td>160.600006</td>\n",
|
||||
" <td>14412086</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-01-31</th>\n",
|
||||
" <td>160.699997</td>\n",
|
||||
" <td>163.000000</td>\n",
|
||||
" <td>164.500000</td>\n",
|
||||
" <td>160.600006</td>\n",
|
||||
" <td>14175405</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-02-01</th>\n",
|
||||
" <td>169.899994</td>\n",
|
||||
" <td>161.000000</td>\n",
|
||||
" <td>172.000000</td>\n",
|
||||
" <td>161.000000</td>\n",
|
||||
" <td>26878438</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-02-02</th>\n",
|
||||
" <td>170.500000</td>\n",
|
||||
" <td>171.000000</td>\n",
|
||||
" <td>173.800003</td>\n",
|
||||
" <td>169.899994</td>\n",
|
||||
" <td>14288239</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-02-05</th>\n",
|
||||
" <td>169.300003</td>\n",
|
||||
" <td>172.000000</td>\n",
|
||||
" <td>172.100006</td>\n",
|
||||
" <td>168.899994</td>\n",
|
||||
" <td>10069132</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>501 rows × 5 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Close Open High Low Volume\n",
|
||||
"Date \n",
|
||||
"2022-02-07 32.560001 31.100000 32.580002 30.500000 14509607\n",
|
||||
"2022-02-08 33.320000 32.580002 33.439999 32.500000 15159861\n",
|
||||
"2022-02-09 33.660000 33.580002 33.880001 33.200001 12581438\n",
|
||||
"2022-02-10 33.720001 33.720001 34.119999 33.360001 13014121\n",
|
||||
"2022-02-11 33.799999 33.320000 33.880001 32.439999 17758379\n",
|
||||
"... ... ... ... ... ...\n",
|
||||
"2024-01-30 163.199997 162.899994 164.500000 160.600006 14412086\n",
|
||||
"2024-01-31 160.699997 163.000000 164.500000 160.600006 14175405\n",
|
||||
"2024-02-01 169.899994 161.000000 172.000000 161.000000 26878438\n",
|
||||
"2024-02-02 170.500000 171.000000 173.800003 169.899994 14288239\n",
|
||||
"2024-02-05 169.300003 172.000000 172.100006 168.899994 10069132\n",
|
||||
"\n",
|
||||
"[501 rows x 5 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "b461abaa-80ef-41d1-b9ad-8b9e5d7e8c7f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"start = \"2023-10-05\"\n",
|
||||
"end = \"2023-11-15\"\n",
|
||||
"\n",
|
||||
"sample = data.loc[start:end]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "2e6c4451-884a-44b4-b149-11e182216ddb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/var/folders/v8/ym6h5fw970l86ns393wxhzm1d3tht0/T/ipykernel_79482/2390513591.py:3: SettingWithCopyWarning: \n",
|
||||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||||
"\n",
|
||||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||||
" sample['Date'] = pd.to_datetime(sample['Date'])\n",
|
||||
"qt.pointer.dispatch: skipping QEventPoint(id=1 ts=0 pos=0,0 scn=610.588,441.089 gbl=610.588,441.089 Released ellipse=(1x1 ∡ 0) vel=0,0 press=-610.588,-441.089 last=-610.588,-441.089 Δ 610.588,441.089) : no target window\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#sample = data\n",
|
||||
"sample.reset_index(inplace=True)\n",
|
||||
"sample['Date'] = pd.to_datetime(sample['Date'])\n",
|
||||
"\n",
|
||||
"fplt.candlestick_ochl(sample)\n",
|
||||
"fplt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "05e50d21-3296-4f08-b571-66abdad2eed1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sample.to_pickle(f\"/Users/tcudikel/Dev/sandbox/trade-strat/data/trend_samples/bearish_kchol_{start}_{end}.pkl\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "1971ae65-a62b-4d35-a117-7b109958a56e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fplt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "5aec2c83-bfac-475a-a6b8-03065138c615",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "tradestrat",
|
||||
"language": "python",
|
||||
"name": "tradestrat"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
0
ops/__init__.py
Normal file
0
ops/__init__.py
Normal file
125
ops/fetch.py
Normal file
125
ops/fetch.py
Normal file
@@ -0,0 +1,125 @@
|
||||
import os
|
||||
from datetime import datetime
|
||||
from typing import Dict
|
||||
|
||||
from tqdm import tqdm
|
||||
from loguru import logger
|
||||
import yfinance as yf
|
||||
import pandas as pd
|
||||
from stocksymbol import StockSymbol
|
||||
|
||||
|
||||
class DataFetcher:
|
||||
def __init__(self):
|
||||
SS_API_KEY = 'a2598c86-da6c-41ac-a792-9af08f3c482c'
|
||||
self.ss_api = StockSymbol(SS_API_KEY)
|
||||
today = datetime.now().strftime("%Y%m%d")
|
||||
self.data_dir = os.path.join('data/tmp', today)
|
||||
|
||||
def get_historical_for(self, ticker, period="1y", interval="1d"):
|
||||
pd.set_option('display.max_rows', None)
|
||||
if type(ticker) != yf.ticker.Ticker:
|
||||
ticker = yf.Ticker(ticker)
|
||||
return ticker.history(period=period, interval=interval)
|
||||
|
||||
def get_historicals_for(self, ticker_list, period="2y", interval="1d"):
|
||||
logger.debug(f"retrieving historical raw ohlc data for tickers:")
|
||||
logger.debug(f"ticker list: {ticker_list}")
|
||||
return yf.download(" ".join(ticker_list), period=period, interval=interval) # to debug: ticker_list[:3]
|
||||
|
||||
def generate_ohlcs_per_ticker(self, combined_data):
|
||||
ohlcs = {}
|
||||
for ticker_symbol in combined_data.columns.get_level_values(1).unique():
|
||||
ohlc_df = combined_data['Close'][ticker_symbol].to_frame().rename(columns={ticker_symbol: 'Close'})
|
||||
ohlc_df['Open'] = combined_data['Open'][ticker_symbol]
|
||||
ohlc_df['High'] = combined_data['High'][ticker_symbol]
|
||||
ohlc_df['Low'] = combined_data['Low'][ticker_symbol]
|
||||
ohlc_df['Volume'] = combined_data['Volume'][ticker_symbol]
|
||||
ohlcs[ticker_symbol] = ohlc_df
|
||||
return ohlcs
|
||||
|
||||
def get_market_list(self):
|
||||
return self.ss_api.market_list
|
||||
|
||||
def get_ticker_list(self, market):
|
||||
self.tickers = []
|
||||
raw_tickers = self.ss_api.get_symbol_list(market=market)
|
||||
for ticker in raw_tickers:
|
||||
self.tickers.append(ticker["symbol"])
|
||||
return self.tickers
|
||||
|
||||
def lookup_tickers(self, symbols):
|
||||
found_tickers = []
|
||||
for symbol in symbols:
|
||||
for item in self.tickers:
|
||||
if symbol in item:
|
||||
#found_tickers[symbol] = yf.Ticker(item)
|
||||
found_tickers.append(yf.Ticker(item))
|
||||
if found_tickers != []:
|
||||
return found_tickers
|
||||
else:
|
||||
return f"Tickers not found for: {symbols}"
|
||||
|
||||
|
||||
def persist_into_pickle(self, combined_data, ohcls_per_ticker):
|
||||
|
||||
logger.debug("saving fetched combined historical price df")
|
||||
os.makedirs(self.data_dir, exist_ok=True)
|
||||
for name, df in combined_data.items():
|
||||
df.to_pickle(f'{self.data_dir}/{name}.pkl')
|
||||
|
||||
logger.debug("saving fetched ohlcs per ticker as seperate dataframes")
|
||||
ohlcs_dir = os.path.join(self.data_dir, "ticker_ohlcs")
|
||||
os.makedirs(ohlcs_dir, exist_ok=True)
|
||||
for name, df in tqdm(ohcls_per_ticker.items(), desc="saving ohlc for each ticker:"):
|
||||
df.to_pickle(f'{ohlcs_dir}/{name}.pkl')
|
||||
|
||||
logger.success(f"all fetched data succesfully saved into: {self.data_dir}")
|
||||
|
||||
|
||||
def check_for_persistent_data(self):
|
||||
logger.info(f"checking hist data for {self.data_dir} exists.. ")
|
||||
|
||||
if not os.path.exists(self.data_dir):
|
||||
logger.warning(f"Tmp data dir missing: {self.data_dir}")
|
||||
return False
|
||||
|
||||
files = os.listdir(self.data_dir)
|
||||
if not any(file.endswith('.pkl') for file in files):
|
||||
logger.warning(f"Persisted OHLC ticker data missing")
|
||||
return False
|
||||
|
||||
ohlcs_dir = os.path.join(self.data_dir, "ticker_ohlcs")
|
||||
if not os.path.exists(ohlcs_dir):
|
||||
logger.warning(f"OHLCs dir not found: {ohlcs_dir}")
|
||||
return False
|
||||
|
||||
pkl_files = [file for file in os.listdir(ohlcs_dir) if file.endswith('.pkl')]
|
||||
if not pkl_files:
|
||||
logger.warning(f"OHLC data files missing")
|
||||
return False
|
||||
|
||||
logger.success("Persisted data found, no need to download again")
|
||||
return True
|
||||
|
||||
|
||||
def load_from_pickle(self):
|
||||
today = datetime.now().strftime("%Y%m%d")
|
||||
data_dir = os.path.join('data/tmp', today)
|
||||
dfs_cv = {}
|
||||
dfs_ohlc = {}
|
||||
items = os.listdir(data_dir)
|
||||
for item in items:
|
||||
if item.endswith('.pkl'):
|
||||
file_path = os.path.join(data_dir, item)
|
||||
df = pd.read_pickle(file_path)
|
||||
dfs_cv[item.replace(".pkl", "")] = df
|
||||
if item == 'ticker_ohlcs':
|
||||
ohlcs_path = os.path.join(data_dir, item)
|
||||
for ticker_file in os.listdir(ohlcs_path):
|
||||
ticker_path = os.path.join(ohlcs_path, ticker_file)
|
||||
df = pd.read_pickle(ticker_path)
|
||||
dfs_ohlc[ticker_file.replace(".pkl", "")] = df
|
||||
return dfs_cv, dfs_ohlc
|
||||
|
||||
|
||||
73
ops/portfolio.py
Normal file
73
ops/portfolio.py
Normal file
@@ -0,0 +1,73 @@
|
||||
from dataclasses import dataclass, field
|
||||
from typing import List
|
||||
|
||||
import yaml
|
||||
#from pydantic import BaseModel, validator
|
||||
|
||||
|
||||
|
||||
@dataclass
|
||||
class Stock:
|
||||
symbol: str
|
||||
quantity: int = 0
|
||||
|
||||
@dataclass
|
||||
class Portfolio:
|
||||
name: str = "dummy_portfolio"
|
||||
stocks: List[Stock] = field(default_factory=list)
|
||||
cash: float = 0.0
|
||||
|
||||
def add_lot(self, stock: Stock):
|
||||
for exist_stock in self.stocks:
|
||||
if exist_stock.symbol == stock.symbol:
|
||||
exist_stock.quantity += stock.quantity
|
||||
break
|
||||
else:
|
||||
self.stocks.append(stock)
|
||||
|
||||
def remove_lot(self, stock: Stock):
|
||||
for exist_stock in self.stocks:
|
||||
if exist_stock.symbol == stock.symbol:
|
||||
if exist_stock.quantity == stock.quantity:
|
||||
self.stocks.remove(stock); break
|
||||
elif exist_stock.quantity < stock.quantity:
|
||||
raise Exception(
|
||||
f"\nInsufficient lots to remove {stock.symbol}: {stock.quantity}\n"
|
||||
f"Current lots for {exist_stock.symbol}: {exist_stock.quantity}"
|
||||
)
|
||||
elif exist_stock.quantity > stock.quantity:
|
||||
exist_stock.quantity -= stock.quantity; break
|
||||
else:
|
||||
raise Exception(f"Given stock not found in portfolio: {exist_stock.symbol}, {exist_stock.quantity}")
|
||||
|
||||
def update_cash(self, amount: float):
|
||||
new_cash = self.cash + amount
|
||||
if new_cash < 0:
|
||||
raise Exception(f"Cannot have a negative cash balance, current balance: {self.cash} - amount:{amount}")
|
||||
self.cash = new_cash
|
||||
|
||||
|
||||
class PortfolioLoader:
|
||||
def __init__(self, path):
|
||||
self.file_path = path
|
||||
|
||||
def load_from_yaml(self):
|
||||
portfolios = {}
|
||||
with open(self.file_path, 'r') as file:
|
||||
data = yaml.safe_load(file)
|
||||
|
||||
if not isinstance(data, dict) or 'portfolios' not in data:
|
||||
raise ValueError("Invalid YAML file format. Expected 'portfolios' key.")
|
||||
|
||||
for portf_decl in data.get('portfolios', []):
|
||||
name = portf_decl.get('name')
|
||||
for stock in portf_decl['stocks']:
|
||||
stock["quantity"] = stock.get('quantity', 0)
|
||||
|
||||
portfolios[name] = Portfolio(
|
||||
name=portf_decl.get('name', None),
|
||||
stocks=[Stock(symbol=stock['symbol'], quantity=stock['quantity']) for stock in portf_decl['stocks']],
|
||||
cash=portf_decl['cash']
|
||||
)
|
||||
|
||||
return portfolios
|
||||
72
ops/strategy.py
Normal file
72
ops/strategy.py
Normal file
@@ -0,0 +1,72 @@
|
||||
import pandas as pd
|
||||
|
||||
from backtesting import Strategy
|
||||
from backtesting.lib import crossover
|
||||
|
||||
|
||||
|
||||
class PCTrader(Strategy):
|
||||
# Define percentate threshold as *class variables*
|
||||
# for later optimization
|
||||
pct = 4
|
||||
|
||||
def PercentChange(self, values):
|
||||
"""
|
||||
Returns percent change of 'values', at
|
||||
each step taking into account previous row value.
|
||||
"""
|
||||
|
||||
return pd.Series(values).pct_change()*100
|
||||
|
||||
def init(self):
|
||||
# Precompute the percent changes
|
||||
self.pc = self.I(self.PercentChange, self.data.Close)
|
||||
|
||||
def next(self):
|
||||
# If percent change is higher than (+) threshold, close any existing
|
||||
# short trades, and buy the asset
|
||||
if self.pc > self.pct:
|
||||
self.position.close()
|
||||
self.buy()
|
||||
|
||||
# Else, if percent change is lower than (-) threshold, close any existing
|
||||
# long trades, and sell the asset
|
||||
elif self.pc < -self.pct:
|
||||
self.position.close()
|
||||
self.sell()
|
||||
|
||||
|
||||
"""import yaml
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict
|
||||
|
||||
|
||||
@dataclass
|
||||
class Strategy:
|
||||
name: str
|
||||
params: Dict[str, Any]
|
||||
|
||||
|
||||
class StrategyLoader:
|
||||
def __init__(self, path):
|
||||
self.file_path = path
|
||||
|
||||
def load_from_yaml(self, name):
|
||||
with open(self.file_path, 'r') as file:
|
||||
data = yaml.safe_load(file)
|
||||
|
||||
strategies = []
|
||||
for strategy_data in data.get('strategies', []):
|
||||
strategy = Strategy(
|
||||
name=strategy_data.get('name', ''),
|
||||
params=strategy_data.get('params', {})
|
||||
)
|
||||
strategies.append(strategy)
|
||||
|
||||
if name:
|
||||
for s in strategies:
|
||||
if s.name == name:
|
||||
return s
|
||||
else:
|
||||
raise Exception(f"No strategy found given name: {name}")"""
|
||||
33
ops/transform.py
Normal file
33
ops/transform.py
Normal file
@@ -0,0 +1,33 @@
|
||||
import pandas as pd
|
||||
|
||||
from ops.fetch import DataFetcher
|
||||
|
||||
|
||||
class DataTransformer:
|
||||
|
||||
def __init__(self):
|
||||
self.data_fetcher = DataFetcher()
|
||||
|
||||
def get_topk_vol_stocks(self, df, K):
|
||||
topk = df.iloc[-1].sort_values(ascending=False).head(K)
|
||||
return pd.DataFrame({
|
||||
'tickers': topk.index,
|
||||
'volumes': topk.values
|
||||
})
|
||||
|
||||
def calc_diffs(self, portfolio):
|
||||
pct_diff = portfolio.pct_change()
|
||||
mon_diff = portfolio.diff()
|
||||
pct_diff.columns = [f"{col}_diff_pct" for col in pct_diff.columns]
|
||||
mon_diff.columns = [f"{col}_diff_mon" for col in mon_diff.columns]
|
||||
|
||||
return pd.concat([portfolio, pct_diff, mon_diff], axis=1)
|
||||
|
||||
def filter_tickers(self, portfolio, raw_data):
|
||||
# TODO: bu kısım daha sonra portfolo objesine göre transform metodu olarak yazılacak
|
||||
#symbols = ["KCHOL", "SAHOL", "FROTO", "TOASO", "TCELL", "TTKOM", "THYAO", "PGSUS"]
|
||||
#tickers = data_fetcher.lookup_tickers(symbols=symbols)
|
||||
#symbol_codes = [t.ticker for t in tickers]
|
||||
#portf_closes = hist_data["closes"][symbol_codes]
|
||||
print("currently not implemented")
|
||||
|
||||
37
preprocess.py
Normal file
37
preprocess.py
Normal file
@@ -0,0 +1,37 @@
|
||||
from loguru import logger
|
||||
|
||||
from ops.transform import DataTransformer
|
||||
from ops.fetch import DataFetcher
|
||||
|
||||
|
||||
|
||||
# INITIALIZE
|
||||
data_fetcher = DataFetcher()
|
||||
data_transformer = DataTransformer()
|
||||
|
||||
|
||||
# FETCH AND PERSIST
|
||||
tickers = data_fetcher.get_ticker_list(market="TR")
|
||||
|
||||
if data_fetcher.check_for_persistent_data():
|
||||
dfs_cv, dfs_ohlc = data_fetcher.load_from_pickle()
|
||||
|
||||
else:
|
||||
raw_data = data_fetcher.get_historicals_for(ticker_list=tickers)
|
||||
ohlcs_data = data_fetcher.generate_ohlcs_per_ticker(combined_data=raw_data)
|
||||
data_fetcher.persist_into_pickle(
|
||||
combined_data={"closes": raw_data["Adj Close"], "volumes": raw_data["Volume"]},
|
||||
ohcls_per_ticker=ohlcs_data
|
||||
)
|
||||
df_cv, df_ohlc = data_fetcher.load_from_pickle()
|
||||
|
||||
# TODO: validation yaz (hist_data ve ohlcs_data için) ve burada validate edip logger.success bas
|
||||
logger.success("historical data preprocessing is done")
|
||||
|
||||
# Refactor Note:
|
||||
# removed this b/c backtesting.py format only require OHLC, gonna transform later
|
||||
#portf_closediffs = data_transformer.calc_diffs(portf_closes)
|
||||
#data_fetcher.persist_into_pickle({"portf_closediffs": portf_closediffs})
|
||||
|
||||
|
||||
|
||||
5
scripts/start_jupy.sh
Executable file
5
scripts/start_jupy.sh
Executable file
@@ -0,0 +1,5 @@
|
||||
#!/bin/bash
|
||||
|
||||
jupyter lab --ip='0.0.0.0' --NotebookApp.token='' --NotebookApp.password='' --no-browser --port=9992 --notebook-dir=/Users/tcudikel/Dev/sandbox/trade-strat
|
||||
|
||||
|
||||
Reference in New Issue
Block a user