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portfolio.py
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portfolio.py
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import pytz
import utils
import plotille
import warnings
import webcolors
import autocolors
import numpy as np
import yfinance as market
from dataclasses import dataclass
from datetime import datetime, timedelta
@dataclass
class Stock:
symbol: str
data: list
def __post_init__(self):
self.curr_value = self.data[-1]
self.open_value = self.data[0]
self.high = max(self.data)
self.low = min(self.data)
self.average = sum(self.data) / len(self.data)
self.change_amount = self.curr_value - self.open_value
self.change_percentage = (self.change_amount / self.curr_value) * 100
return
@dataclass
class PortfolioEntry:
stock: Stock
count: float
average_cost: float
graph: bool = False
color: str = None
def __post_init__(self):
self.holding_market_value = self.stock.curr_value * self.count
self.holding_open_value = self.stock.open_value * self.count
self.cost_basis = self.count * self.average_cost
self.gains = self.holding_market_value - self.cost_basis
if self.count == 0:
self.gains_per_share = 0
else:
self.gains_per_share = self.gains / self.count
return
class Portfolio(metaclass=utils.Singleton):
def __init__(self, *args, **kwargs):
self.stocks = {}
self.open_market_value = 0 # portfolio worth at market open
self.cost_value = (
0 # amount invested into the portfolio (sum of cost of shares)
)
self.market_value = 0 # current market value of shares
return
def add_entry(
self,
stock: Stock,
count: float,
average_buyin_cost: float,
color: str,
graph: bool,
):
entry = PortfolioEntry(stock, count, average_buyin_cost, graph, color)
self.stocks[stock.symbol] = entry
self.open_market_value += entry.holding_open_value
self.market_value += entry.holding_market_value
self.cost_value += entry.cost_basis
return
def get_stocks(self):
return self.stocks
def get_stock(self, symbol):
return self.stocks[symbol]
def average_buyin(self, buys: list, sells: list):
buy_c, buy_p, sell_c, sell_p, count, bought_at = 0, 0, 0, 0, 0, 0
buys = [_.split("@") for _ in ([buys] if type(buys) is not tuple else buys)]
sells = [_.split("@") for _ in ([sells] if type(sells) is not tuple else sells)]
for buy in buys:
next_c = float(buy[0])
if next_c <= 0:
print(
'A negative "buy" key was detected. Use the sell key instead to guarantee accurate calculations.'
)
exit()
buy_c += next_c
buy_p += float(buy[1]) * next_c
for sell in sells:
next_c = float(sell[0])
if next_c <= 0:
print(
'A negative "sell" key was detected. Use the buy key instead to guarantee accurate calculations.'
)
exit()
sell_c += next_c
sell_p += float(sell[1]) * next_c
count = buy_c - sell_c
if count == 0:
return 0, 0
bought_at = (buy_p - sell_p) / count
return count, bought_at
# download all ticker data in a single request
# harder to parse but this provides a signficant performance boost
def download_market_data(self, args, stocks):
# get graph time interval and period
time_period = args.time_period if args.time_period else "1d"
time_interval = args.time_interval if args.time_interval else "1m"
try:
return market.download(
tickers=stocks,
period=time_period,
interval=time_interval,
progress=False,
)
except Exception as e:
print(
"cliStocksTracker must be connected to the internet to function. Please ensure that you are connected to the internet and try again."
)
print("Error message:", e)
def populate(self, stocks_config, args):
sections = stocks_config.sections()
# download all stock data
market_data = self.download_market_data(args, sections)
# iterate through each ticker data
data_key = "Open"
for ticker in sections:
# convert the numpy array into a list of prices while removing NaN values
# if there is only one section, the data frame is not split into tickers
if len(sections) > 1:
data = market_data[data_key][ticker].values[
~np.isnan(market_data[data_key][ticker].values)
]
else:
data = market_data[data_key].values[
~np.isnan(market_data[data_key].values)
]
new_stock = Stock(ticker, data)
# calculate average buy in
buyin = (
stocks_config[ticker]["buy"]
if "buy" in list(stocks_config[ticker].keys())
else ()
)
sellout = (
stocks_config[ticker]["sell"]
if "sell" in list(stocks_config[ticker].keys())
else ()
)
count, bought_at = self.average_buyin(buyin, sellout)
# Check the stock color for graphing
color = (
str(stocks_config[ticker]["color"])
if "color" in list(stocks_config[ticker].keys())
else None
)
# Check that the stock color that was entered is legal
colorWarningFlag = True
if color == None:
colorWarningFlag = False
elif type(color) == str:
if (color.startswith("#")) or (
color in webcolors.CSS3_NAMES_TO_HEX.keys()
):
colorWarningFlag = False
if colorWarningFlag:
warnings.warn(
"The color selected for "
+ ticker
+ " is not in not in the approved list. Automatic color selection will be used."
)
color = None
should_graph = (
"graph" in list(stocks_config[ticker].keys())
and stocks_config[ticker]["graph"] == "True"
)
# finally, add the stock to the portfolio
self.add_entry(new_stock, count, bought_at, color, should_graph)
def gen_graphs(self, independent_graphs, graph_width, graph_height, cfg_timezone):
graphs = []
if not independent_graphs:
graphing_list = []
color_list = []
for sm in self.get_stocks().values():
if sm.graph:
graphing_list.append(sm.stock)
color_list.append(sm.color)
if len(graphing_list) > 0:
graphs.append(
Graph(
graphing_list,
graph_width,
graph_height,
color_list,
timezone=cfg_timezone,
)
)
else:
for sm in self.get_stocks().values():
if sm.graph:
graphs.append(
Graph(
[sm.stock],
graph_width,
graph_height,
[sm.color],
timezone=cfg_timezone,
)
)
for graph in graphs:
graph.gen_graph(autocolors.color_list)
self.graphs = graphs
return
class Graph:
def __init__(
self, stocks: list, width: int, height: int, colors: list, *args, **kwargs
):
self.stocks = stocks
self.graph = ""
self.colors = colors
self.plot = plotille.Figure()
self.plot.width = width
self.plot.height = height
self.plot.color_mode = "rgb"
self.plot.X_label = "Time"
self.plot.Y_label = "Value"
if "timezone" in kwargs.keys():
self.timezone = pytz.timezone(kwargs["timezone"])
else:
self.timezone = pytz.utc
if "starttime" in kwargs.keys():
self.start = (
kwargs["startend"].replace(tzinfo=pytz.utc).astimezone(self.timezone)
)
else:
self.start = (
datetime.now()
.replace(hour=14, minute=30, second=0)
.replace(tzinfo=pytz.utc)
.astimezone(self.timezone)
)
if "endtime" in kwargs.keys():
self.end = (
kwargs["endtime"].replace(tzinfo=pytz.utc).astimezone(self.timezone)
)
else:
self.end = (
datetime.now()
.replace(hour=21, minute=0, second=0)
.replace(tzinfo=pytz.utc)
.astimezone(self.timezone)
)
self.plot.set_x_limits(min_=self.start, max_=self.end)
return
def __call__(self):
return self.graph
def draw(self):
print(self.graph)
return
def gen_graph(self, auto_colors):
self.y_min, self.y_max = self.find_y_range()
self.plot.set_y_limits(min_=self.y_min, max_=self.y_max)
for i, stock in enumerate(self.stocks):
if self.colors[i] == None:
color = webcolors.hex_to_rgb(auto_colors[i % 67])
elif self.colors[i].startswith("#"):
color = webcolors.hex_to_rgb(self.colors[i])
else:
color = webcolors.hex_to_rgb(
webcolors.CSS3_NAMES_TO_HEX[self.colors[i]]
)
self.plot.plot(
[self.start + timedelta(minutes=i) for i in range(len(stock.data))],
stock.data,
lc=color,
label=stock.symbol,
)
self.graph = self.plot.show(legend=True)
return
def find_y_range(self):
y_min = 10000000000000 # Arbitrarily large number (bigger than any single stock should ever be worth)
y_max = 0
for stock in self.stocks:
if y_min > min(stock.data):
y_min = min(stock.data)
if y_max < max(stock.data):
y_max = max(stock.data)
return y_min, y_max