def heatmap():
heatmap_gen, heatmap_stats, years, cols = heatmap_generator()
return render_template("warden/heatmap.html",
title="Monthly Returns HeatMap",
heatmap=heatmap_gen,
heatmap_stats=heatmap_stats,
years=years,
cols=cols,
current_app=current_app,
current_user=fx_rate())
def heatmapbenchmark_json():
# Get portfolio data first
heatmap_gen, heatmap_stats, years, cols = heatmap_generator()
# Now get the ticker information and run comparison
if request.method == "GET":
ticker = request.args.get("ticker")
# Defaults to king BTC
if not ticker:
ticker = "BTC"
# Gather the first trade date in portfolio and store
# used to match the matrixes later
# Panda dataframe with transactions
df = transactions_fx()
# Filter the df acccoring to filter passed as arguments
df["trade_date"] = pd.to_datetime(df["trade_date"])
start_date = df["trade_date"].min()
start_date -= timedelta(days=1) # start on t-1 of first trade
# Generate price Table now for the ticker and trim to match portfolio
fx = current_app.settings['PORTFOLIO']['base_fx']
data = historical_prices(ticker, fx)
mask = data.index >= start_date
data = data.loc[mask]
# If notification is an error, skip this ticker
if data is None:
messages = data.errors
return jsonify(messages)
data = data.rename(columns={'close_converted': ticker + '_price'})
data = data[[ticker + '_price']]
data.sort_index(ascending=True, inplace=True)
data["pchange"] = (data / data.shift(1)) - 1
# Run the mrh function to generate heapmap table
heatmap = mrh.get(data["pchange"], eoy=True)
heatmap_stats = heatmap
cols = [
"Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec",
"eoy",
]
cols_months = [
"Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec",
]
years = heatmap.index.tolist()
# Create summary stats for the Ticker
heatmap_stats["MAX"] = heatmap_stats[heatmap_stats[cols_months] != 0].max(
axis=1)
heatmap_stats["MIN"] = heatmap_stats[heatmap_stats[cols_months] != 0].min(
axis=1)
heatmap_stats["POSITIVES"] = heatmap_stats[
heatmap_stats[cols_months] > 0].count(axis=1)
heatmap_stats["NEGATIVES"] = heatmap_stats[
heatmap_stats[cols_months] < 0].count(axis=1)
heatmap_stats["POS_MEAN"] = heatmap_stats[
heatmap_stats[cols_months] > 0].mean(axis=1)
heatmap_stats["NEG_MEAN"] = heatmap_stats[
heatmap_stats[cols_months] < 0].mean(axis=1)
heatmap_stats["MEAN"] = heatmap_stats[
heatmap_stats[cols_months] != 0].mean(axis=1)
# Create the difference between the 2 df - Pandas is cool!
heatmap_difference = heatmap_gen - heatmap
# return (heatmap, heatmap_stats, years, cols, ticker, heatmap_diff)
return simplejson.dumps(
{
"heatmap": heatmap.to_dict(),
"heatmap_stats": heatmap_stats.to_dict(),
"cols": cols,
"years": years,
"ticker": ticker,
"heatmap_diff": heatmap_difference.to_dict(),
},
ignore_nan=True,
default=datetime.isoformat,
)