Intratio Tutorial & Complete Feature Guide

1. Onboarding & Account Basics #

1.1 Create Account & Verify Email #

Sign up using email or supported social providers (Google / Microsoft / Facebook). Email verification is mandatory for API access and quota tracking.

1.2 Subscription Tiers #

You need the Optimize entitlement for all programmatic endpoints and advanced analytics modules. Visit Pricing to upgrade.

1.3 Retrieving Your API Key

Every request must include an X-API-Key header. Base URL for this session: https://intratio.com/api/v1.

1.4 Core Data Objects

• Company: Fundamental snapshot + trailing numeric metrics.
• Signal: Forecast value per horizon (3_months, 1_month, 2_weeks, 1_week, 3_days) and optionally alpha-adjusted variants.
• Portfolio Optimization: Mean-variance style solver leveraging forecast-derived expected returns, constraints and objective variants.
• Analytics Metrics: Distributions, correlations, outliers, trends, change detection, percentiles and peers aggregation.

2. Navigating the User Interface #

2.1 Stock Forecast Pages

Each ticker page consolidates latest multi-horizon forecasts, historical signal trajectory, fundamental highlights and percentile placements versus sector & industry.

2.2 Screeners

Forecast Screener: Rank by direction & intensity across horizons. Company Screener: Filter metrics using range syntax (e.g. Gross_Margin_percentage__gte=40).

2.3 Following Tickers

Use follow/unfollow buttons or endpoints to curate a watchlist. Improves signal change triage on login.

3. Core Forecast & Metric Concepts #

3.1 Forecast Horizons

Horizon keys: 3_months, 1_month, 2_weeks, 1_week, 3_days. Alpha versions normalize relative to baseline risk/benchmark behaviour.

3.2 Signal Quality & Change

Change compares successive dates; Trend accumulates over a window (fast SQL variant). Use both to detect emerging acceleration.

3.3 Percentiles

Percentile = percentage of observations strictly below the company value. Provided for All, Sector and Industry contexts.

3.4 Aggregations

Sector & industry endpoints return primarily Median to reduce outlier distortion. Dispersion endpoints show spread (std, IQR) enabling factor crowding detection.

4. API Quick Start #

Base URL: https://intratio.com/api/v1 — Add trailing slash & query parameters as needed.

curlcurl -s -H "X-API-Key: YOUR_API_KEY" https://intratio.com/api/v1/signals/top/?horizon=3_months | jq

pythonimport requests
API_KEY='YOUR_API_KEY'; BASE='https://intratio.com/api/v1'
r = requests.get(f"{BASE}/companies/AAPL/", headers={'X-API-Key':API_KEY})
print(r.json()['ticker'], 'loaded')

jsconst API_KEY='YOUR_API_KEY'; const BASE='https://intratio.com/api/v1';
fetch(`${BASE}/metrics/distribution/?metric=Gross_Margin_percentage`,{headers:{'X-API-Key':API_KEY}})
  .then(r=>r.json()).then(console.log);

4.1 Essential Endpoints

4.2 Authentication Pattern

Use a thin wrapper to attach headers; rotate keys by regenerating in profile (coming soon) without downtime.

5. Core Analytical Workflows #

6. Portfolio Optimization #

The optimizer maps selected tickers to expected returns derived from the latest horizon forecast (raw or alpha). Objectives:

• MaximizeReturnToRisk (maximum Sharpe)
• MinimizeRisk (efficient risk given target return)
• MaximizeReturn (efficient return given target risk)

Optional constraints: weight bounds, total cash out (capital deployment), target portfolio beta.

pythonimport requests, json
payload={
  "tickers":["AAPL","MSFT","GOOGL"],
  "horizon":"3_months",
  "alpha":false,
  "objective":"MaximizeReturnToRisk",
  "weight_bounds":[0,1],
  "target_risk":10,
  "target_return":10
}
res=requests.post("https://intratio.com/api/v1/portfolio/optimize/",headers={'X-API-Key':'YOUR_API_KEY'},json=payload)
print(res.json()['performance'])

6.1 Interpreting Output

6.2 Best Practices

• Exclude tickers missing forecasts (endpoint returns errors for missing).
• Apply metric filters for quality (e.g. Debt_per_Equity upper bound).
• Rebalance when /signals/change/ indicates drift beyond threshold.

7. Advanced Analytics Modules #

The Analytics Dashboard renders interactive Plotly visuals powered by the same endpoints:

• Sectors & Industries Ranking: Median aggregation by selected metric.
• Signals Distribution: Central tendency & tail profile for horizon.
• Metric Distribution & Outliers: Robust trimming & hist fences.
• Correlation Matrix: Multi-metric relationship map.
• Scatter & Regression: Quick factor scatter with linear fit & correlation.
• Percentile Compare: Multi-ticker relative positioning across metrics.
• Signals Change & Trend: Short term shock vs multi-bar momentum.
• Company Snapshot: 20-key metrics vs peer median.

7.1 Extending Analytics

Copy network calls from browser DevTools (all /api/v1/...)—compose bespoke research notebooks using identical JSON contracts.

8. Monitoring, Watchlists & Risk Flags #

Design a lightweight monitoring loop:

pythonimport requests, time
API='YOUR_API_KEY'; BASE='https://intratio.com/api/v1'
WATCH=['AAPL','MSFT','NVDA']
while True:
  ch=requests.get(f"{BASE}/signals/change/?horizon=3_months",headers={'X-API-Key':API}).json()['results']
  flagged=[r for r in ch if r['ticker'] in WATCH and abs(r['change'])>0.05]
  if flagged: print('Rebalance candidates:', flagged)
  time.sleep(3600)

Combine with /metrics/outliers/ to dynamically exclude deteriorating quality names.

9. Data Integrity & Limitations #

• Daily forecast refresh—intraday calls may not shift values until next batch.
• Median aggregation over raw averages to damp outlier skew.
• Percentiles strict-below methodology (ties cluster at same rank threshold).
• Missing metrics return null; propagate carefully in custom analytics.

10. Roadmap & Extensibility #

11. FAQ #

How often should I re-run optimization?

Common cadence: daily after forecast refresh, plus on significant /signals/change/ divergences.

Raw vs Alpha forecast?

Alpha attempts to isolate stock-specific return component; use in diversified baskets to reduce systematic drift.

Why medians everywhere?

Robust central tendency—less sensitivity to single distortive outlier values, improving comparative stability.

Percentile vs Rank?

Percentile gives continuous 0–100 scale; you can derive rank by ordering on value if discrete ordering required.

12. System Architecture & Data Flow #

This section clarifies how raw inputs become actionable forecasts and portfolio outputs so you can reason about latency, consistency, and extensibility.

13. Forecast Methodology & Interpretation #

13.1 Signal Scale

Signals represent an expected relative directional magnitude. They are not guaranteed percentage returns; instead they rank order opportunities. Empirically the top decile historically outperforms median baskets after transaction cost adjustments (internal research; forthcoming whitepaper).

13.2 Confidence & Reliability

Not all horizons have equal predictive stability. Longer horizons incorporate more noise; use convergence across at least two adjacent horizons for higher conviction. Future release: explicit confidence field beta.

13.3 Alpha Variant

The alpha=true variant neutralizes broad market and sector factor exposures using rolling factor betas. Residual emphasis reduces systematic drift but slightly lowers raw magnitude. Use when constructing diversified multi-sector portfolios.

13.4 Limitations & Risks

• Regime Shifts: Structural breaks (macro shocks, policy changes) can reduce model fit until adaptation cycle completes.
• Data Gaps: Newly listed tickers with sparse historical fundamentals yield lower quality signals.
• Survivorship: Downstream backtests must incorporate delisted names to avoid bias (future extended dataset export).

14. API Deep Dive (Auth, Pagination, Filtering) #

14.1 Authentication

Supply X-API-Key with every request. Unauthorized responses: 401. Invalid or missing key: 403. Rotate keys (coming soon) with zero downtime by creating overlapping validity windows.

14.2 Pagination

List endpoints (e.g. /companies/, /signals/top/) implement standard limit/offset parameters: ?limit=100&offset=200. Default limit often 50. Always code defensively against truncated result sets.

pythonimport requests
API='YOUR_API_KEY'; BASE='https://intratio.com/api/v1'
all_rows=[]; offset=0
while True:
  r=requests.get(f"{BASE}/signals/top/?horizon=3_months&limit=100&offset={offset}",headers={'X-API-Key':API}).json()
  all_rows.extend(r['results'])
  if not r.get('next'): break
  offset+=100
print('Loaded', len(all_rows))

14.3 Filtering Patterns

• Range: Gross_Margin_percentage__gte=40
• In-list: ticker__in=AAPL,MSFT,NVDA
• Case-insensitive contains: name__icontains=pharma

14.4 Rate Limiting

Response headers expose X-RateLimit-Remaining, X-RateLimit-Reset. On 429, backoff until reset epoch. Avoid burst loops; batch tickers or use vector endpoints.

14.5 Error Codes

15. Data Dictionary (Selected Metrics) #

Key standardized fields. (Extended downloadable dictionary forthcoming.)

16. Backtesting & Performance Evaluation #

16.1 Principles

• Use point-in-time forecasts only (historical snapshot endpoints forthcoming).
• Incorporate realistic slippage & transaction costs.
• Beware look-ahead leakage (delay trade execution until next session open).

import pandas as pd, requests, time
API='YOUR_API_KEY'; BASE='https://intratio.com/api/v1'
# 1. Fetch daily top decile forecasts (simplified)
raw=requests.get(f"{BASE}/signals/top/?horizon=3_months&limit=100",headers={'X-API-Key':API}).json()['results']
df=pd.DataFrame(raw)
# 2. Hypothetical next-day price change (placeholder; replace with market data join)
df['fwd_return']=0.01  # mock
# 3. Evaluate mean realized return of top basket
print('Mean fwd return', df['fwd_return'].mean())

For each backtest date D:
  Pull forecasts published at D (point-in-time snapshot)
  Select top N by horizon score
  Execute at next open (D+1) or VWAP (model) with cost deduction
  Hold until exit rule (time-based or signal decay)
  Record realized returns & risk metrics
Aggregate performance: CAGR, volatility, Sharpe, max drawdown, turnover

16.2 Key Metrics

• Hit Rate: % of positions with positive realized return.
• Information Coefficient: Rank correlation between forecast and realized horizon return.
• Turnover: (Sum of absolute weight changes)/2; controls transaction costs.
• Capacity: Liquidity constraints (shares traded vs ADV) if scaling strategy.

17. Advanced Portfolio Techniques #

17.1 Constraints

• Per-asset bounds weight_bounds
• Group / sector exposure caps (coming)
• Target portfolio beta ranges
• Minimum forecast quality score (future)

17.2 Scenario Stress (Manual)

Adjust expected returns downward for vulnerable sectors under hypothetical macro shocks; rerun optimizer to produce defensive allocation.

python# Example manual scenario tweak
for t in payload['tickers']:
  if t in CYCLICALS: scenario_expected_returns[t]*=0.7

17.3 Rebalancing Triggers

• Signal Drift: Forecast percentile change > 20 pts.
• Risk Drift: Portfolio volatility estimate +15% vs baseline.
• Correlation Regime: Mean pairwise correlation shift > threshold.

18. Integration Recipes (Python, JS, Excel, Airflow) #

pythonclass IntratioClient:
  def __init__(self, key, base='https://intratio.com/api/v1'):
    self.key=key; self.base=base
  def _get(self, path, **params):
    import requests; r=requests.get(f"{self.base}{path}",params=params,headers={'X-API-Key':self.key}); r.raise_for_status(); return r.json()
  def top_signals(self,horizon='3_months',limit=50):
    return self._get('/signals/top/', horizon=horizon, limit=limit)
client=IntratioClient('YOUR_API_KEY')
print(client.top_signals()[:2])

jsimport fetch from 'node-fetch';
const KEY='YOUR_API_KEY', BASE='https://intratio.com/api/v1';
const res=await fetch(`${BASE}/signals/worst/?horizon=1_month`,{headers:{'X-API-Key':KEY}});
console.log(await res.json());

18.3 Excel / Power Query

mlet
  Source = Json.Document(Web.Contents("https://intratio.com/api/v1/signals/top/?horizon=3_months", [Headers=[X-API-Key="YOUR_API_KEY"]])),
  Results = Source[results]
in Results

18.4 Airflow DAG Snippet

pythonfrom airflow import DAG
from airflow.operators.python import PythonOperator
import requests, pendulum
API='YOUR_API_KEY'; BASE='https://intratio.com/api/v1'

def pull_signals():
  r=requests.get(f"{BASE}/signals/top/?horizon=3_months",headers={'X-API-Key':API}).json()
  # store to warehouse...

with DAG('intratio_daily_signals', start_date=pendulum.today('UTC').add(days=-1), schedule='0 5 * * *', catchup=False) as dag:
  PythonOperator(task_id='fetch', python_callable=pull_signals)

19. Automation & Alerting #

While native webhooks are on the roadmap, interim automation can poll endpoints and dispatch notifications.

pythonimport requests, smtplib, time
API='YOUR_API_KEY'; BASE='https://intratio.com/api/v1'
THRESH=0.07
while True:
  ch=requests.get(f"{BASE}/signals/change/?horizon=3_months",headers={'X-API-Key':API}).json()['results']
  movers=[c for c in ch if abs(c['change'])>THRESH]
  if movers:
    body='\n'.join(f"{m['ticker']} {m['change']:.2%}" for m in movers)
    # send email (pseudo)
    print('ALERT:\n'+body)
  time.sleep(3600)

20. Security, Compliance & Governance #

• Transport: All endpoints over HTTPS; enforce SSL redirect in production.
• Key Storage: Store API key in environment variables or secret manager (never commit to version control).
• Least Privilege: Separate keys for staging vs production workloads.
• Audit: Keep internal logs of programmatic calls & derived signals used in investment decisions.
• Compliance: Combine with internal restricted lists to pre-filter tickers before optimization.

21. Troubleshooting & Diagnostics #

22. Glossary #

23. Extension & Contribution Guide #

Architectural extensibility points (roadmap aligns with these hooks):

• Custom Factor Uploads: Compose weighted blends (e.g. quality composite) for percentile ranking (planned).
• User Webhooks: Push-based event stream for signal change triggers.
• Scenario Engine: Inject stress vectors into optimizer expected return and covariance inputs.
• Client SDKs: Official Python / JS wrappers to reduce boilerplate.

24. Quick Start Pathways #

25. Summary Checklist #

• Confirm API key & test authentication.
• Fetch initial forecasts & identify candidate tickers.
• Validate fundamentals (margin, leverage percentile).
• Run baseline portfolio optimization.
• Set monitoring loop & alert thresholds.
• Iterate performance evaluation (hit rate, IC, Sharpe).
• Refine constraints & integrate scenario adjustments.
• Document process & compliance considerations.