Ict Algorithmic Theory Calculator
Analyze price delivery algorithms using ICT algorithmic theory (time, price, liquidity). Enter values for instant results with step-by-step formulas.
Reviewed for accuracy by Daniel Agrici, Founder & Lead Developer
Ict Algorithmic Theory Calculator
Calculator
Adjust values & calculateEnter your values below. Every result is computed in your browser — no data is sent to any server.
Formula: Efficiency = Time Phase Alignment + Price Zone Alignment + Liquidity Sweep Confirmation + Bias Alignment
Worked example — Phase: Distribution | Target: 1.1080 | Efficiency: 75% | Setup: High probability long
Formula
Efficiency = Time Phase Alignment + Price Zone Alignment + Liquidity Sweep Confirmation + Bias Alignment
Where Time Phase checks if the current session matches the expected AMD stage, Price Zone verifies premium/discount positioning relative to bias, Liquidity Sweep confirms if stop runs have occurred, and Bias Alignment ensures the setup matches the higher timeframe direction.
Worked Examples
Example 1: Bullish AMD Analysis at NY Open
Problem:EUR/USD daily range is 1.0950-1.1080. Asian range is 1.0980-1.1010. Current price is 1.0970 at NY open with bullish bias. Analyze the algorithmic setup.
Solution:Daily range: 130 pips | Asian range: 30 pips Price position: 15.4% (deep discount) AMD Phase: Distribution (NY open timing) Asian low sweep: Price below 1.0980 = sell-side liquidity taken Stop run target: 1.0980 - 9 pips = 1.0971 Primary target: 1.1080 (buy-side liquidity) Secondary target: 1.1080 + 35 pips = 1.1115 Efficiency score: High (discount + bias aligned + liquidity swept)
Result:Phase: Distribution | Target: 1.1080 | Efficiency: 75% | Setup: High probability long
Example 2: Bearish Manipulation Detection During London
Problem:GBP/USD range 1.2650-1.2800. Asian range 1.2720-1.2750. Price is 1.2760 during London open. Bearish bias. Identify manipulation.
Solution:Daily range: 150 pips | Asian range: 30 pips Price position: 73.3% (premium zone) AMD Phase: Manipulation (London open timing) Price above Asian high (1.2750) = buy-side liquidity sweep Stop run target: 1.2750 + 9 pips = 1.2759 Primary target: 1.2650 (sell-side liquidity) Secondary target: 1.2650 - 40 pips = 1.2610 Manipulation phase = false breakout above Asian high likely
Result:Phase: Manipulation | Expect reversal | Target: 1.2650 | Setup: Wait for market structure shift
Frequently Asked Questions
What is ICT Algorithmic Theory and how does it explain price movement?
ICT Algorithmic Theory proposes that financial markets are not moved randomly by supply and demand alone but are driven by sophisticated algorithms operated by central banks, market makers, and large financial institutions. These algorithms deliver price according to three primary variables: time, price, and liquidity. The theory suggests that price delivery follows predictable patterns based on the time of day, the location of resting liquidity (stop losses), and institutional pricing models. Understanding how these algorithms function allows traders to anticipate where price is likely to go next rather than reacting to past price action. The framework provides a structured way to analyze markets using concepts like the Power of Three, fair value gaps, and institutional order flow.
What is the Power of Three (AMD) cycle in ICT trading?
The Power of Three, also known as AMD (Accumulation, Manipulation, Distribution), describes the three-phase cycle that institutional algorithms use to deliver price each trading day. During Accumulation, typically occurring in the Asian session, smart money quietly builds positions within a tight range, establishing the setup for the day. Manipulation follows during the London open or early New York session, where the algorithm runs price against the intended direction to sweep stop losses and trigger retail traders into the wrong side of the market. Distribution is the final phase where price moves strongly in the intended direction, delivering profits to institutional positions established during accumulation. This cycle repeats daily, weekly, and on intraday timeframes.
How do algorithms use liquidity pools to drive price delivery?
Algorithms target liquidity pools because institutional orders are too large to be filled at a single price level and require the opposite side of the trade to execute. Buy-side liquidity exists above swing highs and resistance levels where buy stop orders cluster, including stop losses from short traders and breakout buy orders from momentum traders. Sell-side liquidity sits below swing lows and support levels where sell stop orders accumulate. The algorithm drives price toward these pools to match institutional orders against the available liquidity. For example, a bullish algorithm will first drive price below an obvious support level to trigger sell stops, filling institutional buy orders against those stop-triggered sells, before reversing price upward toward the intended bullish target.
What role does the Asian session range play in ICT Algorithmic Theory?
The Asian session range serves as the blueprint for daily algorithmic price delivery in ICT theory. It represents the accumulation phase of the Power of Three cycle, where institutional positions are quietly established within a defined range. The Asian range high and low create the first liquidity targets of the day, and the direction in which the algorithm sweeps these levels often reveals the true directional intent. If the algorithm sweeps below the Asian low first and then reverses strongly, it suggests bullish intent as sell-side liquidity was collected to fill institutional buy orders. The width of the Asian range also indicates expected daily volatility, with wider Asian ranges suggesting larger standard deviation projections for the remainder of the trading day.
How does time of day affect algorithmic price delivery?
Time of day is a critical variable in ICT Algorithmic Theory because different phases of the AMD cycle are concentrated in specific time windows. The Asian session (8 PM to 2 AM EST) corresponds primarily to accumulation, where 80 percent of the time is spent building positions in a range. The London open (2 AM to 5 AM EST) is the primary manipulation window, where 70 percent of the activity involves stop runs and false moves designed to trap retail traders. The New York session (8 AM to 12 PM EST) is predominantly distribution, where 70 percent of the activity delivers price in the intended direction. Understanding these time-based probabilities helps traders avoid taking trades during manipulation phases and focus entries during the transition from manipulation to distribution.
What is institutional pricing and how do premium and discount zones work?
Institutional pricing divides the current dealing range into zones based on fair value. The equilibrium or midpoint of the daily range represents fair value. Everything above equilibrium is the premium zone, where prices are considered expensive, and institutional algorithms prefer to sell or distribute positions. Everything below equilibrium is the discount zone, where prices are considered cheap, and algorithms prefer to buy or accumulate positions. The deep premium zone (top 10 percent of the range) and deep discount zone (bottom 10 percent) represent extreme pricing where reversals are most likely. ICT teaches that algorithms always seek to return price to equilibrium and that institutional traders only buy in discount zones and sell in premium zones, unlike retail traders who chase breakouts.
How does the previous daily close relate to algorithmic price delivery?
The previous daily close is a key reference point in ICT Algorithmic Theory because it represents the settlement price around which institutional positions are marked to market. Price often gravitates toward the previous close during the early stages of a new trading day as algorithms seek to rebalance around this level. When price is significantly above the previous close, it suggests premium pricing and potential distribution. When price is significantly below, it indicates discount pricing and potential accumulation. The relationship between the current price and the previous close also helps determine gap fill probability, as algorithms tend to seek efficiency by filling price gaps left between the previous close and the current session open.
What is algorithmic efficiency and why does it matter for trade quality?
Algorithmic efficiency refers to how well the current market conditions align with the expected algorithmic price delivery model. A highly efficient setup occurs when multiple factors converge: the time of day matches the expected AMD phase, price is in the correct zone (discount for buys, premium for sells), liquidity has been recently swept, and the direction aligns with the higher timeframe bias. Traders can score efficiency by checking these confluences. A score above 70 suggests strong alignment with algorithmic delivery and higher probability trades. Below 40 suggests conflicting signals and lower probability setups. This scoring system helps traders filter out marginal setups and focus capital on the highest-quality opportunities where the algorithm is most likely to deliver predictable price action.
How do fair value gaps fit into the algorithmic theory framework?
Fair value gaps (FVGs) are a direct manifestation of algorithmic price delivery in ICT theory. They form when the algorithm displaces price so quickly that a gap appears between three consecutive candles, where the wick of candle one does not overlap with the wick of candle three. These gaps represent inefficient price delivery that the algorithm is programmed to revisit and fill, making them powerful levels for entries on retracements. In the algorithmic framework, FVGs form during the distribution phase when institutional orders create strong directional displacement. The algorithm then returns price to these gaps during subsequent pullbacks to offer additional entry opportunities at fair value. Bullish FVGs in discount zones and bearish FVGs in premium zones represent the highest-probability setups.
Can ICT Algorithmic Theory be applied to markets other than forex?
ICT Algorithmic Theory applies to any liquid market where institutional algorithms participate in price delivery, including stock indices (S&P 500, Nasdaq), commodities (gold, oil, natural gas), bonds, and individual stocks with sufficient institutional volume. The core concepts of time-based delivery, liquidity targeting, and the AMD cycle are universal because they reflect how large institutions must execute orders regardless of the asset class. Stock indices show particularly clean algorithmic patterns due to the high institutional participation and the clear daily opening and closing times. Gold (XAU/USD) follows similar patterns to forex pairs during the London and New York sessions. The main adjustment required is understanding each market's specific session times and typical daily range characteristics.
References
Reviewed for accuracy by Daniel Agrici, Founder & Lead Developer · Editorial policy
Related Calculators
🧮Ict Master Setup Calculator
The ultimate ICT setup validator — input market structure, FVG, order blocks, and killzone timing to score trade probability using Inner Circle Trader methodology.
🧮Ict Accumulation Distribution Calculator
Identify Wyckoff accumulation and distribution phases using ICT market structure analysis.
🧮Ict London Close Killzone Calculator
Calculate London close killzone times and identify reversal setups in the last hour of London.
🧮Ict Macro Time Calculator
Calculate ICT macro time windows at :50-:10 of each hour for high-probability entries.
🧮Ict Seasonal Tendency Calculator
Calculate seasonal bias for major pairs using ICT quarterly and monthly seasonal tendencies.
🧮Ict Standard Deviation Calculator
Calculate standard deviation projections from Asian range for ICT daily range expansion.
🧮Ict Rejection Block Calculator
Identify rejection blocks (wicks into FVGs) for ICT-based trade entry signals.
🧮Ict Dealing Range Calculator
Calculate the weekly dealing range boundaries using ICT institutional orderflow concepts.