1. Collect current market information
The process begins with data such as candles, price, volume, trades, order-book depth, funding, open interest and liquidations. Some systems also evaluate large trades, taker flow and cumulative volume delta.
Every output is limited by the timeliness and quality of these inputs. Missing, delayed or erroneous source data can affect the assessment.
2. Convert raw data into features
Raw numbers are transformed into features that describe trend, momentum, volatility, market structure and derivatives activity. A feature might identify a moving-average relationship, a support break, increasing open interest or an imbalance in aggressive flow.
Features often need normalization so that values measured in different units can be compared without one dominating merely because its raw number is larger.
3. Evaluate bullish, bearish and neutral evidence
The model assigns directional meaning to the features. Some inputs may support a bullish case, others a bearish case, and others may be neutral. The system can weight these inputs based on importance, reliability or market regime.
Conflicting evidence should not be hidden. It can lower confidence, narrow the probability difference or produce a CHOP assessment.
4. Produce direction and probability context
After combining the evidence, the system can produce a direction label and Long and Short probabilities. Thresholds may be used to prevent tiny score differences from being presented as strong signals.
CHOP is a useful output when neither side has sufficient separation or the market regime is unsuitable for a directional assessment.
5. Generate reference levels
The system may derive an entry region, targets, stop and invalidation level from support, resistance, volatility and market structure. These levels describe the model's current thesis; they do not select the user's leverage or position size.
A stop level also cannot guarantee the execution price. Fast markets can create slippage, gaps or exchange delays.
6. Refresh as conditions change
Live systems recalculate when new market information arrives. Direction, probability, strength and levels may change. A market can shift from trend to range or from balanced flow to aggressive one-sided activity.
The current output should therefore be read with its timestamp. A stale signal may no longer describe the market.
7. Present evidence, not only the answer
An explainable workspace should show the supporting context: timeframes, trend, momentum, flow, liquidations, open interest, funding and depth. This enables the user to understand why the model currently leans in one direction.
A black-box label without context makes it difficult to recognize disagreement or model deterioration.
8. Separate analysis from execution
ScalperBaba does not connect to an exchange account or execute trades. The user independently decides whether to act, which exchange to use, the order type, position size, leverage and risk controls.
This separation is important because no model knows the user's financial situation, legal eligibility, portfolio exposure or loss tolerance.
Common reasons signals fail
- The market regime changes faster than the model adapts.
- A news event overwhelms prior technical evidence.
- Liquidity becomes thin and slippage increases.
- Input data is delayed, incomplete or temporarily unavailable.
- The signal is correct directionally but the timing or invalidation level fails.
- Excessive leverage turns an ordinary adverse move into liquidation.
See the concepts inside one market-intelligence workspace.
ScalperBaba organizes signals, Long and Short probability, technical structure, derivatives flow, liquidations, open interest, funding and order-book depth across supported USDT perpetual markets.