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.
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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.
Last reviewed: December 2025
Worked Examples
Example 1: Bullish AMD Analysis at NY Open
Example 2: Bearish Manipulation Detection During London
Background & Theory
The Ict Algorithmic Theory Calculator applies the following established principles and formulas. Foreign exchange markets facilitate the conversion of one currency into another and serve as the largest and most liquid financial markets in the world, with daily turnover exceeding seven trillion US dollars. Exchange rates are quoted as currency pairs, expressing the price of one unit of a base currency in terms of a quote currency. For example, a EUR/USD rate of 1.0850 means one euro buys 1.0850 US dollars. The smallest standardized price movement in most pairs is the pip, typically the fourth decimal place, with a value of 0.0001 per unit for USD-denominated pairs. The bid price is the rate at which a dealer will buy the base currency, while the ask price is the rate at which it will sell. The spread between bid and ask represents the dealer's compensation and varies with liquidity and volatility. Leverage amplifies both gains and losses by allowing traders to control positions larger than their deposited margin. A 100:1 leverage ratio means a one-percent adverse move eliminates the entire margin, making position sizing and risk management critical. Two parity conditions from international economics anchor exchange rate theory. Purchasing Power Parity (PPP) holds that exchange rates should adjust over time so that identical goods trade at equivalent prices across countries: S = P_d / P_f, where S is the spot rate and P_d and P_f are domestic and foreign price levels. PPP performs well over long horizons but poorly in the short run due to trade barriers, non-tradable goods, and capital flows. Covered Interest Rate Parity (CIRP) is a near-arbitrage condition stating that forward exchange rate premiums or discounts exactly offset interest rate differentials between two currencies: F/S = (1 + r_d) / (1 + r_f). Deviations from CIRP create riskless arbitrage opportunities that traders rapidly eliminate. Uncovered Interest Rate Parity posits that high-yielding currencies should depreciate to offset their interest advantage, though empirical evidence is mixed and the carry trade — borrowing in low-rate currencies to invest in high-rate ones — has generated persistent returns.
History
The history behind the Ict Algorithmic Theory Calculator traces back through the following developments. For much of the nineteenth century and early twentieth century, the international monetary system operated under the classical gold standard, under which each participating currency was fixed to a defined weight of gold, making bilateral exchange rates effectively constant. The system provided price stability and facilitated global trade but constrained governments' ability to respond to economic downturns. World War One shattered the gold standard as nations suspended convertibility to finance wartime expenditures. The interwar period saw attempts to restore gold convertibility, most notably the British return to the gold standard in 1925 at the pre-war parity, a decision criticized by John Maynard Keynes as deflationary. The Great Depression forced widespread currency devaluations and the effective collapse of the international gold standard by the early 1930s. The Bretton Woods Conference of July 1944 established a new order in which member currencies were pegged to the US dollar, while the dollar alone was convertible into gold at 35 dollars per troy ounce. The International Monetary Fund and World Bank were created at the same conference to oversee the system. Bretton Woods delivered exchange rate stability during the postwar growth era but came under strain as US deficits and European dollar accumulation outpaced American gold reserves. On August 15, 1971, President Nixon announced the suspension of dollar-gold convertibility — the so-called Nixon Shock — effectively ending the Bretton Woods system. By 1973, major currencies had transitioned to floating exchange rates determined by market supply and demand, a regime that has persisted. On September 16, 1992, hedge fund manager George Soros shorted the British pound against the European Exchange Rate Mechanism constraints, forcing the UK's withdrawal in what became known as Black Wednesday. Electronic trading platforms emerged in the 1990s and 2000s, replacing voice-brokered interbank markets and dramatically reducing transaction costs for institutional and retail participants alike.
Frequently Asked Questions
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\nPrice position: 15.4% (deep discount)\nAMD Phase: Distribution (NY open timing)\nAsian low sweep: Price below 1.0980 = sell-side liquidity taken\nStop run target: 1.0980 - 9 pips = 1.0971\nPrimary target: 1.1080 (buy-side liquidity)\nSecondary target: 1.1080 + 35 pips = 1.1115\nEfficiency 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\nPrice position: 73.3% (premium zone)\nAMD Phase: Manipulation (London open timing)\nPrice above Asian high (1.2750) = buy-side liquidity sweep\nStop run target: 1.2750 + 9 pips = 1.2759\nPrimary target: 1.2650 (sell-side liquidity)\nSecondary target: 1.2650 - 40 pips = 1.2610\nManipulation 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.
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 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.
References
Reviewed by Daniel Agrici, Founder & Lead Developer · Editorial policy