def show_patterns(stdscr):
"""Ticker correlation matrix data table.
"""
rng = pd.date_range(utc_dtdate() - timedelta(days=1),
periods=24,
freq='1H')
df = price_df(CMC["CORR"], rng)
corr = df.pct_change().corr().round(2)
headers = [" "] + corr.index.tolist()
rows, colors = [], []
for idx in corr.index.tolist():
row = [str(round(Decimal(n), 2)) for n in corr[idx].tolist()]
rows.append([idx] + row)
colors.append([c.WHITE] + [coeff_color(n) for n in corr[idx].tolist()])
title = "Price Correlation Matrix (CORR30)"
footer = "Dataset frequency: %s, Datapoints: %s" % ('?', len(df))
stdscr.clear()
print_table(stdscr, [title],
headers,
rows,
colors,
align=str.rjust,
colsp=2)
stdscr.addstr(stdscr.getyx()[0] + 1,
int(midx(stdscr) / 2 - len(footer) / 2), footer)
# Biggest winners/losers datatable
ex = forex.getrate('CAD', utc_dtdate())
stdscr.addstr(stdscr.getyx()[0] + 1, stdscr.getyx()[1], "")
deviants = tickers.volatile_24h()
print_table(stdscr, ["Winners & Losers in Top 500"],
["Coin", "Rank", "Price", "Market Cap", "24 Hour"], [[
n["symbol"], n["rank"],
pretty(ex * to_int(n["price_usd"]), t='money', abbr=True),
pretty(ex * to_int(n["mktcap_usd"]), t='money', abbr=True),
format(n["pct_24h"], "+.2f") + "%"
] for n in deviants],
[[c.WHITE, c.WHITE, c.WHITE, c.WHITE,
pnlcolor(n["pct_24h"])] for n in deviants],
align=str.rjust,
colsp=2)
def show_watchlist(stdscr):
diff = tickers.diff
db = get_db()
ex = forex.getrate('CAD', utc_dtdate())
rows, colors = [], []
hdr = [
"Rank", "Sym", "Price", "Market Cap", "24h Vol", "1 Hour", "24 Hour",
"7 Days", "30 Days", "1 Year"
]
updated = []
for watch in db.watchlist.find():
cursor = db.cmc_tick.find({
"symbol": watch["symbol"]
}).sort("date", -1).limit(1)
if cursor.count() < 1:
continue
tckr = cursor.next()
rows.append([
tckr["rank"], tckr["symbol"], tckr["price_usd"],
tckr["mktcap_usd"], tckr["vol_24h_usd"], tckr["pct_1h"],
tckr["pct_24h"], tckr["pct_7d"],
diff(tckr["symbol"],
tckr["price_usd"],
"30D",
to_format="percentage"),
diff(tckr["symbol"],
tckr["price_usd"],
"1Y",
to_format="percentage")
])
updated.append(tckr["date"].timestamp())
rows = sorted(rows, key=lambda x: int(x[0]))
for row in rows:
colors.append(
[c.WHITE]*5 +\
[pnlcolor(row[5]), pnlcolor(row[6]), pnlcolor(row[7]),
pnlcolor(row[8]), pnlcolor(row[9])]
)
row[2] = pretty(ex * row[2], t='money')
row[3] = pretty(ex * to_int(row[3]), t='money', abbr=True)
row[4] = pretty(ex * to_int(row[4]), t='money', abbr=True)
row[5] = pretty(row[5], t='pct', f='sign')
row[6] = pretty(row[6], t='pct', f='sign')
row[7] = pretty(row[7], t='pct', f='sign')
row[8] = pretty(row[8], t='pct', f='sign')
row[9] = pretty(row[9], t='pct', f='sign')
# Print
stdscr.clear()
updated = to_relative_str(utc_datetime() - to_dt(max(updated))) + " ago"
stdscr.addstr(0, 2, "Updated %s" % updated)
stdscr.addstr(0, stdscr.getmaxyx()[1] - 5, CURRENCY.upper())
stdscr.addstr(1, 0, "")
print_table(stdscr, ["Watchlist"], hdr, rows, colors, div=True)
def corr_minmax_history(symbol, start, freq, max_rank):
delta = parse_period(freq)[2]
_date = start
results = []
while _date < utc_dtdate():
results.append(corr_minmax(symbol, _date, _date + delta, max_rank))
_date += delta
return results
def show_history(stdscr, symbol):
log.info("Querying %s ticker history", symbol)
t1 = Timer()
db = get_db()
ex = forex.getrate('CAD', utc_dtdate())
n_display = 95
colspace = 3
indent = 2
hdr = ['Date', 'Open', 'High', 'Low', 'Close', 'Market Cap', 'Vol 24h']
tickerdata = db.cmc_tick.find({
"symbol": symbol
}).sort('date', -1).limit(n_display)
n_datarows = tickerdata.count()
log.debug("%s tickers queried in %sms", tickerdata.count(), t1)
if tickerdata.count() == 0:
log.info("No ticker history found for %s", symbol)
return False
strrows = []
for tck in tickerdata:
strrows.append([
tck['date'].strftime("%m-%d-%Y"),
pretty(tck['open'], t="money"),
pretty(tck['high'], t="money"),
pretty(tck['low'], t="money"),
pretty(tck['close'], t="money"),
pretty(tck['mktcap_usd'], t="money", abbr=True),
pretty(tck['vol_24h_usd'], t="money", abbr=True)
])
import curses
from app.screen import divider, printrow, _colsizes
colwidths = _colsizes(hdr, strrows)
stdscr.clear()
stdscr.addstr(0, indent, "%s History" % symbol)
printrow(stdscr, 2, hdr, colwidths, [c.WHITE for n in hdr], colspace)
divider(stdscr, 3, colwidths, colspace)
for i in range(0, len(strrows)):
colors = [c.WHITE for n in range(0, 7)]
printrow(stdscr, i + 4, strrows[i], colwidths, colors, colspace)
n_rem_scroll = n_datarows - (curses.LINES - 4)
log.info("n_datarows=%s, n_rem_scroll=%s", n_datarows, n_rem_scroll)
return n_rem_scroll
def show_portfolio(stdscr):
diff = tickers.diff
t1 = Timer()
db = get_db()
total = 0.0
profit = 0
datarows, updated = [], []
ex = forex.getrate('CAD', utc_dtdate())
hdr = [
'Rank', 'Sym', 'Price', 'Mcap', 'Vol 24h', '1 Hour', '24 Hour',
'7 Day', '30 Days', '3 Months', 'Amount', 'Value', '/100'
]
# Build datarows
for hold in db.portfolio.find():
cursor = db.cmc_tick.find({
"symbol": hold["symbol"]
}).sort("date", -1).limit(1)
if cursor.count() < 1: continue
tckr = cursor.next()
value = round(hold['amount'] * ex * tckr['price_usd'], 2)
profit += (tckr['pct_24h'] / 100) * value if tckr['pct_24h'] else 0.0
total += value
updated.append(tckr["date"].timestamp())
datarows.append([
tckr['rank'], tckr['symbol'], ex * round(tckr['price_usd'], 2),
ex * tckr.get('mktcap_usd', 0), ex * tckr["vol_24h_usd"],
tckr["pct_1h"], tckr["pct_24h"], tckr["pct_7d"],
diff(tckr["symbol"],
tckr["price_usd"],
"30D",
to_format="percentage"),
diff(tckr["symbol"],
tckr["price_usd"],
"90D",
to_format="percentage"), hold['amount'], value, None
])
# Calculate porfolio %
for datarow in datarows:
datarow[12] = round((float(datarow[11]) / total) * 100, 2)
# Sort by value
datarows = sorted(datarows, key=lambda x: int(x[11]))[::-1]
rows, colors = [], []
for datarow in datarows:
colors.append([c.WHITE] * 5 +
[pnlcolor(datarow[n])
for n in range(5, 10)] + [c.WHITE] * 3)
rows.append([
datarow[0], datarow[1],
pretty(datarow[2], t='money'),
pretty(datarow[3], t='money', abbr=True),
pretty(datarow[4], t='money', abbr=True),
pretty(datarow[5], t='pct', f='sign'),
pretty(datarow[6], t='pct', f='sign'),
pretty(datarow[7], t='pct', f='sign'),
pretty(datarow[8], t='pct', f='sign'),
pretty(datarow[9], t='pct', f='sign'),
pretty(datarow[10], abbr=True),
pretty(datarow[11], t='money'),
pretty(datarow[12], t='pct')
])
# Print title Row
stdscr.clear()
updated = to_relative_str(utc_datetime() - to_dt(max(updated))) + " ago"
stdscr.addstr(0, 2, "Updated %s" % updated)
stdscr.addstr(0, stdscr.getmaxyx()[1] - 5, CURRENCY.upper())
stdscr.addstr(1, 0, "")
# Portfolio datatable
print_table(stdscr, ["Portfolio"],
hdr,
rows,
colors,
align='right',
colsp=2,
div=True)
stdscr.addstr(stdscr.getyx()[0] + 1, 0, "")
# Summary table
print_table(stdscr, ["Summary"], ["Holdings", "24 Hour", "Total Value"], [[
len(datarows),
pretty(int(profit), t="money", f='sign', d=0),
pretty(total, t='money')
]], [[c.WHITE, pnlcolor(profit), c.WHITE]],
div=True)
def show_markets(stdscr):
"""Global market data.
"""
_diff = tickers.mkt_diff
_to = pretty
db = get_db()
stdscr.clear()
# Latest market (table)
ex = forex.getrate('CAD', utc_dtdate())
hdr = [
'Market Cap', '24h Volume', 'BTC Dominance', 'Markets', 'Currencies',
'1 Hour', '24 Hour', '7 Day', '30 Day'
]
mktdata = list(db.cmc_mkt.find().limit(1).sort('date', -1))
if len(mktdata) == 0:
return log.error("db.cmc_mkt empty")
rows, colors = [], []
for mkt in mktdata:
rows.append([
_to(ex * mkt['mktcap_usd'], t="money", abbr=True),
_to(ex * mkt['vol_24h_usd'], t="money", abbr=True),
_to(mkt['pct_mktcap_btc'], t="pct"),
_to(mkt['n_markets'], d=0),
_to(mkt['n_assets'] + mkt['n_currencies'], d=0),
_to(_diff('1H', to_format='percentage'), t="pct", f="sign"),
_to(_diff('24H', to_format='percentage'), t="pct", f="sign"),
_to(_diff('7D', to_format='percentage'), t="pct", f="sign"), "-"
])
colors.append([c.WHITE] * 5 +
[pnlcolor(rows[-1][col]) for col in range(5, 9)])
stdscr.addstr(0, 2, "< Home")
page_title = "Markets (%s)" % CURRENCY.upper()
stdscr.addstr(0, int(stdscr.getmaxyx()[1] / 2 - len(page_title) / 2),
page_title)
updated = "Updated " + to_relative_str(utc_datetime() -
mktdata[0]["date"]) + " ago"
stdscr.addstr(0, stdscr.getmaxyx()[1] - len(updated) - 2, updated)
stdscr.addstr(2, 0, "")
print_table(stdscr, ["Current"], hdr, rows, colors, div=True)
# Weekly market (table)
start = utc_dtdate() + timedelta(days=-14)
cursor = db.cmc_mkt.find({
"date": {
"$gte": start,
"$lt": utc_dtdate()
}
}).sort('date', -1)
if cursor.count() < 1:
return log.error("no data for weekly markets")
hdr = [
"Date", "Mcap Open", "Mcap High", "Mcap Low", "Mcap Close",
"Mcap Spread", "Mcap SD", "Volume", "BTC Dom"
]
rows, colors = [], []
for mkt in cursor:
ex = forex.getrate('CAD', mkt["date"])
rows.append([
mkt["date"].strftime("%b-%d"),
_to(ex * mkt["mktcap_open_usd"], t='money', d=1, abbr=True),
_to(ex * mkt["mktcap_high_usd"], t='money', d=1, abbr=True),
_to(ex * mkt["mktcap_low_usd"], t='money', d=1, abbr=True),
_to(ex * mkt["mktcap_close_usd"], t='money', d=1, abbr=True),
_to(mkt["mktcap_spread_usd"] / mkt["mktcap_low_usd"] * 100,
t='pct'),
_to(ex * mkt["mktcap_std_24h_usd"], t='money', d=1, abbr=True),
_to(ex * mkt['vol_24h_close_usd'], t="money", d=1, abbr=True),
_to(ex * mkt['btc_mcap'], t="pct")
])
colors.append([c.WHITE] * 9)
cursor.rewind()
df = pd.DataFrame(list(cursor))
colors[df["mktcap_high_usd"].idxmax()][2] = c.GREEN
colors[df["mktcap_low_usd"].idxmin()][3] = c.RED
colors[df["vol_24h_close_usd"].idxmax()][7] = c.GREEN
stdscr.addstr(stdscr.getyx()[0] + 1, 0, "")
print_table(stdscr, ["Recent"], hdr, rows, colors, div=True)