Stock price prediction is one of the most challenging problems in machine learning. Markets are noisy, non-stationary, and influenced by everything from quarterly earnings to geopolitical events. Yet despite this complexity, ML models can identify patterns in historical data that carry real predictive signal — if engineered carefully.
This article explains exactly how Stocker's prediction engine works: what data it ingests, how features are engineered, which models are used, and how the final 30-day forecast is assembled.
The 4-Step Prediction Pipeline
Every time you run an analysis on Stocker, the following pipeline executes in sequence:
Step 1 — Data Collection
The foundation of any good ML model is high-quality data. Stocker fetches data from two sources: Yahoo Finance as the primary source, with Stooq as a fallback for historical OHLCV data when Yahoo rate-limits or returns incomplete results.
OHLCV History
Open, High, Low, Close, Volume — at least 60 trading days required. More history means better feature quality.
Fundamentals
P/E ratio, EPS, revenue growth, market cap, beta — contextualises the stock within its sector.
Analyst Ratings
Strong buy / buy / hold / sell counts from Wall Street analysts, fetched from Yahoo Finance QuoteSummary API.
News & Sentiment
Recent headlines scored with VADER sentiment analysis — captures market mood beyond price action alone.
Minimum data threshold: Stocker requires at least 60 trading days of history. Newly listed stocks or tickers with sparse data will return an error rather than produce an unreliable forecast.
Step 2 — Feature Engineering
Raw price data alone has limited predictive power. The real work happens in feature engineering — transforming OHLCV bars into a rich set of signals that the model can learn from.
Price-Derived Features
These features capture the direction and magnitude of recent price movements.
log_returnLog of daily return — normalises scale across stocks
lag_1 … lag_5Previous 1–5 day close prices as direct features
sma_20, sma_5020 and 50 day Simple Moving Averages
ema_12, ema_26Exponential MAs used to compute MACD signal
price_vs_sma20% distance of price from its 20-day average
high_low_rangeDaily candle range as % of close
Momentum Indicators
Momentum features capture the speed and direction of recent price trends.
rsi_14Relative Strength Index (14 period) — overbought/oversold
macdMACD line (EMA12 − EMA26)
macd_signal9-day EMA of MACD — crossover signal
momentum_55-day price momentum (close / close_5d_ago − 1)
momentum_1010-day price momentum
stoch_kStochastic %K — position within recent high/low range
Volatility Features
Volatility features help the model understand regime — is the market calm or turbulent?
bb_upper / bb_lowerBollinger Bands (20-day, 2σ) — dynamic support/resistance
bb_widthBand width as % of midline — volatility regime
atr_14Average True Range — absolute daily volatility
rolling_std_2020-day rolling standard deviation of returns
Volume Features
Volume confirms price moves — a breakout on high volume is more reliable than one on thin volume.
volume_ratioToday's volume / 20-day average volume
obvOn-Balance Volume — cumulative buy/sell pressure
vwap_ratioPrice vs VWAP — institutional entry/exit levels
volume_trend5-day volume slope — accelerating or decelerating interest
Step 3 — The Ensemble Model
Stocker trains three separate models on the feature frame, each with different learning biases. The final prediction is the average of all three — a technique called ensemble averaging that reduces variance and produces more stable forecasts than any single model.
L1/L2 regularisation
Gradient-based sampling
Maximum diversity
Why three models?
Each model makes different assumptions about the data. XGBoost and LightGBM are gradient-boosted trees that build models sequentially, each new tree correcting the errors of the previous ones. ExtraTreesRegressor builds trees with randomised splits, injecting diversity into the ensemble.
When models with different biases agree on a direction, the signal is stronger. When they disagree, the average dampens the extreme prediction — protecting against model-specific overfitting.
Step 4 — Recursive Forecasting
Predicting one day ahead is straightforward — use today's features to predict tomorrow's price. But predicting 30 days ahead is harder because features like lag_1 and rsi_14 depend on prices we haven't seen yet.
Stocker solves this with recursive forecasting:
Predict Day 1 using known historical features.
Append the Day 1 prediction to the price series. Recompute all features (SMA, RSI, lags etc.) on the extended series.
Predict Day 2 using the updated feature set. Repeat for all 30 trading days.
Error accumulation: Each recursive step compounds the prediction error of the previous step. This means near-term forecasts (1–7 days) are typically more accurate than far-term forecasts (20–30 days). Treat the 30-day chart as a directional trend estimate, not a precise price target.
How Accurate Are the Predictions?
Stocker's models are evaluated on out-of-sample test sets for each stock individually. Typical accuracy metrics on a 7-day horizon for large-cap S&P 500 stocks:
* Indicative averages across S&P 500 backtests. Performance varies significantly by stock, sector, and market regime. Past model performance does not guarantee future accuracy.
What AI Cannot Predict
No ML model can predict the following — and Stocker's is no exception:
Black Swan Events
Pandemics, sudden regulatory bans, geopolitical shocks — events with no historical precedent.
Earnings Surprises
A company beating or missing estimates by a wide margin can move a stock 15–30% overnight.
Central Bank Decisions
Unexpected rate changes affect the entire market simultaneously — not captured by individual stock features.
Institutional Flow
Dark pool trades and large block orders are not visible in public OHLCV data.
See it in action
Run a free AI prediction on any US or Turkish stock — no account required.
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