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fix: stabilize and correct dividend forecasting algorithm #131

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fix: stabilize and correct dividend forecasting algorithm #131

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9 changes: 5 additions & 4 deletions backend/ui/static/js/specialSitTool.js
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Original file line number Diff line number Diff line change
Expand Up @@ -898,7 +898,8 @@ function exportSSDivCSV() {
row.push(item.last_ex_date || '-');
row.push(item.last_amount || '-');
row.push(item.is_above_2_percent ? 'Yes' : 'No');
row.push(item.expected_amount || '-');
let expectedCSV = item.expected_amount ? `${item.expected_amount} (${item.expected_type || 'Interim'})` : '-';
row.push(expectedCSV);
row.push(item.expected_highly_likely || '-');
row.push(item.expected_less_likely || '-');

Expand Down Expand Up @@ -1166,10 +1167,10 @@ function renderSSDividends() {
}
}

let expectedAmountHTML = item.expected_amount ? parseFloat(item.expected_amount).toFixed(2) : '-';
if (item.expected_amount && item.last_amount) {
let expectedAmountHTML = item.expected_amount ? `${parseFloat(item.expected_amount).toFixed(2)} <span style="font-size: 0.8em; color: #aaa;">(${item.expected_type || 'Interim'})</span>` : '-';
if (item.expected_amount && item.expected_amount_compare) {
let numExpected = parseFloat(item.expected_amount);
let numLast = parseFloat(item.last_amount);
let numLast = parseFloat(item.expected_amount_compare);
if (numExpected > numLast) {
expectedAmountHTML = `${expectedAmountHTML} <span style="color: #60a5fa; margin-left: 5px;">&#8593;</span>`; // Up arrow blue
} else if (numExpected < numLast) {
Expand Down
58 changes: 38 additions & 20 deletions backend/web/api/data/special_sit_routes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -184,6 +184,8 @@ def circ_diff(d1, d2):
expected_amount = None
expected_highly_likely = None
expected_less_likely = None
expected_type = None
expected_amount_compare = None

if history:
# Most recent overall dividend (just for table display purposes)
Expand All @@ -196,36 +198,45 @@ def circ_diff(d1, d2):
# Sort ascending for cycle processing
history_asc = sorted(history, key=lambda x: x['ex_date_obj'] if x['ex_date_obj'] else datetime.date.min)

# Cluster historical dividends into "Cycles" based on Month mapping (approximate)
# We will use the month of the ex_date as the primary key for cycles
# To account for dividends shifting by a few days (e.g. May 30th to June 2nd)
# We map the Month, but with a +/- 30 day tolerance.
final_cluster = []
interim_clusters = []

clusters = []
five_years_ago = today - datetime.timedelta(days=365*5)
recent_hist = [h for h in history_asc if h['ex_date_obj'] and h['ex_date_obj'] >= five_years_ago]

for h in recent_hist:
doy = get_doy(h['ex_date_obj'])
placed = False
for c in clusters:
mean_doy = sum(get_doy(x['ex_date_obj']) for x in c) / len(c)
if circ_diff(doy, mean_doy) <= 45: # 45 days threshold to group shifting months
# Avoid clustering multiple dividends from the exact same year in one cluster
# (e.g. if there's a special and final in the same month)
if not any(x['ex_date_obj'].year == h['ex_date_obj'].year for x in c):
c.append(h)
placed = True
break
if not placed:
clusters.append([h])
# Skip special dividends entirely for forecasting
if 'special' in (h.get('purpose') or '').lower() or h.get('dividend_type') == 'Special':
continue

if h.get('dividend_type') == 'Final':
final_cluster.append(h)
else:
doy = get_doy(h['ex_date_obj'])
placed = False
for c in interim_clusters:
mean_doy = sum(get_doy(x['ex_date_obj']) for x in c) / len(c)
if circ_diff(doy, mean_doy) <= 90: # 90 days threshold to handle larger shifts like May->June
if not any(x['ex_date_obj'].year == h['ex_date_obj'].year for x in c):
c.append(h)
placed = True
break
if not placed:
interim_clusters.append([h])

clusters = [final_cluster] + interim_clusters if final_cluster else interim_clusters

# For each cycle, find its next upcoming date
upcoming_cycles = []
for c in clusters:
if not c: continue
most_recent = c[-1]
mr_date = most_recent['ex_date_obj']

# Skip clusters that haven't paid in the last 2 years (kill the cycle)
if mr_date.year < today.year - 1:
continue

if mr_date >= today:
# Already announced for future
next_date = mr_date
Expand Down Expand Up @@ -271,6 +282,7 @@ def circ_diff(d1, d2):

if years_diff >= 1 and prev_amt and curr_amt and prev_amt > 0:
cagr = ((curr_amt / prev_amt) ** (1 / years_diff)) - 1
cagr = min(max(cagr, -1.0), 0.5) # Cap CAGR between -100% and +50%
growth_rates.append(cagr)

avg_growth = np.mean(growth_rates) if growth_rates else 0
Expand All @@ -284,9 +296,11 @@ def circ_diff(d1, d2):
upcoming_cycles.append({
'next_date': next_date,
'is_announced': is_announced,
'exp_amt': exp_amt,
'exp_amt': None if is_announced else exp_amt,
'highly_likely_month': highly_likely_month,
'less_likely_months': less_likely_m
'less_likely_months': less_likely_m,
'type': most_recent.get('dividend_type') or 'Interim',
'last_amt_in_cycle': most_recent['amount']
})

# Pick the chronologically next cycle
Expand All @@ -296,6 +310,8 @@ def circ_diff(d1, d2):

if next_cycle['exp_amt'] is not None:
expected_amount = round(next_cycle['exp_amt'], 2)
expected_amount_compare = next_cycle['last_amt_in_cycle']
expected_type = next_cycle['type']

if next_cycle['is_announced']:
expected_highly_likely = f"Announced: {next_cycle['next_date'].strftime('%d-%m-%Y')}"
Expand All @@ -319,6 +335,8 @@ def circ_diff(d1, d2):
"last_amount": last_amount,
"is_above_2_percent": is_above_2_percent,
"expected_amount": expected_amount,
"expected_amount_compare": expected_amount_compare,
"expected_type": expected_type,
"expected_highly_likely": expected_highly_likely,
"expected_less_likely": expected_less_likely,
"history": history
Expand Down
95 changes: 0 additions & 95 deletions patch_js.py
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10 changes: 0 additions & 10 deletions test.py
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