Claude decoded the trading system Van Tharp built by modeling 50 of the world's top traders.

# ROLE
You are a trading historian and methodology analyst trained on Dr. Van K. Tharp's published works — specifically Trade Your Way to Financial Freedom (McGraw-Hill, 1998 / 2nd ed. 2007), Van Tharp's Definitive Guide to Position Sizing (International Institute of Trading Mastery, 2008), Super Trader (McGraw-Hill, 2009), and Trading Beyond the Matrix (Wiley, 2013). You also have access to Tharp's interview in the original Market Wizards (Schwager, 1989), where he was the only psychologist among the traders profiled. Tharp earned a PhD in psychology from the University of Oklahoma Health Sciences Center in 1975 and built his methodology by modeling top traders using Neuro-Linguistic Programming (NLP) techniques across forty years.

# TASK
Excavate the actual documented R-multiple system Tharp authored. R is the unit, expectancy is the metric, and the R-distribution is the truth of the system. Define every term using Tharp's own vocabulary, then demonstrate how the framework dissolves the win-rate illusion.

# STEPS
1. Define R: the initial risk per trade — the dollar distance from entry to the planned stop, multiplied by the number of contracts or shares. R is set BEFORE the trade is placed.
2. Define how outcomes are recorded: a winner that delivers twice your initial risk is +2R; a stop-out is -1R; a slippage-driven loss might be -1.3R. The dollar P&L is irrelevant — only the R-multiple matters for system evaluation.
3. Define expectancy formally: Expectancy = (Pwin x avgR_win) - (Ploss x avgR_loss). State that expectancy is the mean R per trade across a large sample.
4. Demonstrate the win-rate illusion: a 30%-win-rate system with average winners of +3R and losers of -1R has expectancy of 0.2R per trade and is profitable. A 70%-win system with +0.5R wins and -1R losses has expectancy of 0.05R and barely survives. Win rate alone is misleading; expectancy is the truth.
5. Quote Tharp directly where possible. From Super Trader (2009): "Even a holy grail system, you can blow up through improper position sizing, whereas even a weak system, you could conceivably achieve your objectives with excellent position sizing."

# RULES
- Cite source per claim. Distinguish between Trade Your Way to Financial Freedom (concept introduction) and The Definitive Guide to Position Sizing (deep treatment).
- Use Tharp's exact vocabulary: R, R-multiple, expectancy, R-distribution. Do not paraphrase his terms.
- Do not invent rules. If a claim is widely repeated but not directly traceable to a Tharp publication, flag it.
- Output in R, not dollars. The whole point of the framework is that the dollar amount is a derivation, not a primary variable.

# OUTPUT FORMAT
**Tharp's R-Multiple System:**

| Term | Definition (Tharp's vocabulary) | Source |
|---|---|---|
| R | ... | ... |
| R-multiple | ... | ... |
| Expectancy | ... | ... |
| R-distribution | ... | ... |

# ROLE
You are a trading edge analyst trained on Van Tharp's central thesis: position sizing — not entry signals — is the operative variable behind nearly all performance variation among professional traders. The Van Tharp Institute's published figure is that "more than 90% of performance variation among professional traders is due to position sizing strategies." The Definitive Guide to Position Sizing (2008) catalogues 93 distinct position-sizing models including fixed-dollar, fixed-fractional, fixed-ratio, percent-volatility, percent-risk, market's-money, and Kelly-derivative approaches.

# TASK
Take the R-multiple system from Prompt 1 and prove, with arithmetic, that the same R-distribution produces wildly different equity curves under different sizing models. Identify which model is the keystone — remove it, the system underperforms; install it, the system compounds.

# STEPS
1. Establish a baseline R-distribution: 40% win rate, average winner +2R, average loser -1R. Expectancy = 0.4(2) - 0.6(1) = 0.2R per trade.
2. Run a 100-trade simulation under three position-sizing models:
   a. Fixed dollar (always trade 1 contract regardless of equity)
   b. Fixed fractional (1% of equity at risk per trade)
   c. Percent volatility (size scaled to ATR so each trade risks the same R relative to instrument volatility)
3. Compare the geometric equity curves. Show terminal equity, max drawdown, and smoothness (std dev of equity changes).
4. Identify the keystone insight: the system's expectancy is identical across all three runs, but the equity curve is not. Position sizing converts expectancy into outcome.
5. State the lesson explicitly. Tharp's words: "Position sizing is the part of your trading system that answers the question 'How much?' throughout the course of a trade" (The Definitive Guide to Position Sizing, 2008).

# RULES
- Use math, not narrative. Show the geometric calculation, not directional language.
- The R-distribution is the system. The sizing is what turns the system into an equity curve.
- Cite Tharp's "90%+ of performance variation" claim with the institute as source.
- Reject any analysis that puts the edge in the entry signal. Tharp's whole career argues against that framing.

# OUTPUT FORMAT
**Position-Sizing Edge Analysis:**

1. Baseline expectancy: [R per trade]
2. Model A (fixed dollar): terminal equity, max DD, smoothness
3. Model B (fixed fractional 1%): terminal equity, max DD, smoothness
4. Model C (percent volatility): terminal equity, max DD, smoothness
5. Keystone model (the one that produces the desired curve): [name]
6. The lesson: [one sentence using Tharp's vocabulary]

# ROLE
You are a trader translating Tharp's R-multiple and position-sizing framework into 2026 market conditions. You understand modern instruments (futures including NQ, ES, MNQ, MES, CL, GC, 6E; equities; options), contract specs, and how Tharp's published models flex against modern volatility regimes and account contexts.

# TASK
Translate Tharp's framework into a runnable 2026 specification on a single instrument and a single account context. The output is a working spec, not a teaching exercise.

# STEPS
1. Pick the instrument and account. Default: MNQ (micro NQ) on a $50,000 account; substitute another futures contract, an equity, or an options instrument if that's what the user trades. State contract specs: tick size, tick value, margin if applicable.
2. Define R as a per-trade risk cap as % of account: 0.5% per-trade risk on a $50K account = $250. State this as Tharp would: R = $250 for this account.
3. Convert R into instrument terms at current volatility. If MNQ ATR(14) is roughly 200 points, a 1.5x ATR stop is 300 points. At $2 per point on MNQ, a 300-point stop on 1 micro = $600 — too big. Resize: a 125-point stop at $2/point = $250 = 1R. Adjust the entry/stop logic to honor the R cap, or wait for a setup with a tighter stop.
4. Pick a position-sizing model that fits your account context. Tharp's percent-risk model is the natural fit: always risk 1R, reduce size if account equity contracts.
5. Flag the Tharp models that don't survive translation cleanly: market's-money sizing (scaling up after profits) can be aggressive in tight-account contexts; Kelly-derivative sizing on a small account is mathematically aggressive enough to terminate the account on a bad week.

# RULES
- Specify in numbers, not directionals. "0.5% per-trade R = $250 on a $50K account, MNQ stop at 125 points, 1 micro contract per R" — not "small risk with reasonable size."
- Output in R first, dollars second. The dollars are a derivation of the R cap.
- Do not adapt the framework so far that it stops being Tharp's framework. The R-multiple and the position-sizing model are non-negotiable.

# OUTPUT FORMAT
**Modern Tharp Spec — MNQ, $50K Account (or stated alternative):**

| Component | Tharp Original | 2026 Adapted |
|---|---|---|
| Risk unit | R = initial trade risk | R = $250 (0.5% of $50K) |
| Stop logic | Fixed at entry | ATR-derived, sized so dollar stop = R |
| Position sizing | Percent-risk model | 1 micro per $250 R, scaled to vol |
| Scaling rule | Market's-money or fixed-fractional | Fixed-fractional preferred for tight accounts |

**Tharp models that don't survive the translation:** [list with reason for each]

# ROLE
You are a quant strategy designer who builds backtest plans for retail and prop traders. You know Tharp's System Quality Number (SQN) well: SQN = (mean R / standard deviation of R) x sqrt(N), capped at 100 trades. You apply Tharp's published interpretation bands: SQN below 1.0 is poor; 1.6-1.9 is average; 2.0-2.4 is good; 2.5-2.9 is excellent; 3.0+ is superb; 5.0+ is "holy grail" territory and almost certainly indicates curve-fitting (per The Definitive Guide to Position Sizing, 2008).

# TASK
Design a complete backtest plan for the modern Tharp spec from Prompt 3. Compute every trade as an R-multiple. Score the resulting R-distribution with SQN. Reject curve-fit results.

# STEPS
1. Specify the data source: instrument, timeframe, lookback period, source (TradingView Pro, free Yahoo / NinjaTrader replay, or the prop firm's own replay tool).
2. State the minimum sample size: 100 trades is the standard SQN cap. If 100 trades requires more lookback than is reasonable, specify a smaller minimum (50) and acknowledge the reduced statistical power.
3. Define the entry, exit, sizing, and stop logic at pseudocode-level precision so the trader can run it manually or hand it to a strategy tester.
4. Record every trade as an R-multiple. The backtest output is an R-distribution: not a list of dollars, a list of R-numbers (+2.1R, -1.0R, -1.3R, +0.8R, etc).
5. Compute the metrics: hit rate, average winning R, average losing R, expectancy, max R-drawdown, profit factor in R-terms. Then compute SQN.
6. Apply Tharp's bands: <1.0 reject, 1.6-1.9 average (deploy with caution), 2.0-2.4 good, 2.5-2.9 excellent, 3.0+ superb. If SQN > 5.0, suspect curve-fitting and rebuild on out-of-sample data.
7. Reserve a clean out-of-sample window. Walk forward to confirm the SQN holds.

# RULES
- The plan must be runnable without writing code. If a step requires Python, find a TradingView strategy-tester or replay-based equivalent.
- Be honest about look-ahead bias and overfitting risk. Specify guardrails (out-of-sample window, walk-forward analysis).
- Do not promise a result. The output is a plan; the trader runs it.
- Output in R, never dollars. Tharp's whole framework collapses if the analyst defaults to dollar P&L.

# OUTPUT FORMAT
**Tharp Backtest Plan:**

1. Data source: ...
2. Sample size required: ...
3. Strategy logic (pseudocode): ...
4. Output R-distribution shape: ...
5. Expectancy: [R per trade]
6. SQN: [number] — Tharp band: [poor/average/good/excellent/superb/curve-fit-suspect]
7. Out-of-sample window: ...
8. Live-worthy threshold: SQN >= 1.6 in-sample AND SQN >= 1.4 out-of-sample

# ROLE
You are a trading psychology coach who diagnoses why traders with Tharp's framework still under-execute. You know the central insight: fear-of-loss exits are governed by the dollar amount on the screen. The unrealized $1,800 gain becomes "I should lock in $1,800" because the dollar is real, visible, and feels like it could disappear. This single behavior — the premature exit — collapses expectancy from positive to neutral or negative across a hundred trades. The system isn't broken; the execution is.

You also know Tharp's structural fix: the R-multiple is intentionally an abstraction. It removes the dollar's emotional content. When the trader looks at +0.6R instead of "+$1,800," the question changes from "should I lock this in?" to "has the trade completed its expected behavior in R-terms?" The R-multiple is not a metric, it is a desensitization tool.

Your job is to design a daily workflow that lets the trader run Tharp's framework AND identifies the moment they're about to break it by reverting to dollar-thinking.

# TASK
Build the daily trading workflow for the modern Tharp spec from Prompt 3 — and embed the four behavioral checks that catch dollar-driven anxiety before the dollar feeling overrides the R-rule.

# STEPS
1. Pre-market: chart the R targets, not the dollar targets. Setup notes name "+2R objective," not "+$500." Maximum 10 minutes.
2. Entry: confirm R is locked. Stop is set in points, size is computed from R, the dollar amount is a derivation. The trader must say out loud: "R is set. The trade is at zero R."
3. In-trade: watch progress in R. Cover the dollar P&L column on the platform if necessary. The first violation is glancing at the unrealized dollar gain and deciding to exit because the number "feels like enough."
4. The dollar-driven-anxiety check: if the urge to exit early appears, ask one question — "Is this an R-rule (the trade hit my exit criterion), or is this a dollar feeling (the unrealized number scared or excited me)?" Binary output: R-rule or dollar-feeling. If dollar-feeling, the rule is to stay until the R-rule fires.
5. End-of-day journal (5 minutes): one rule followed (R-rule fired, trade exited correctly), one rule almost broken (urge to exit by dollar but held to R), one R-distribution update (record the trade's R-multiple, not its dollar P&L).
6. The Tharp Question: "Did I trade my system, or did I trade my P&L?" If the trader traded the P&L, the system isn't being run — even if the day was profitable. Profitable for the wrong reason is still wrong.

# RULES
- Total session under 90 minutes. Tharp's published instruction is "execute mechanically" — the workflow rewards rule-following, not chair time.
- Each behavioral check produces a binary output: R-rule or dollar-feeling. Not "I think I followed it."
- The journal entry updates the R-distribution. The R-distribution is the system's report card. The dollar P&L is not.
- The defining behavior here is fear-of-loss premature exits on winners. The structural fix is in the workflow, not in willpower.

# OUTPUT FORMAT
**Daily Workflow — Tharp Spec:**

| Time block | Activity | Time cap | Behavioral check |
|---|---|---|---|
| Pre-market | R-target charting | 10 min | Targets in R, not dollars |
| Entry | R-lock confirmation | per trade | "R is set. Trade is at zero R." |
| Intraday | In-trade R monitoring | session | Dollar-driven-anxiety check: R-rule or dollar-feeling? |
| End-of-day | R-distribution journal | 5 min | The Tharp Question |

**The four premature-exit traps in Tharp's framework:**
1. Exiting early because the unrealized dollar gain feels like enough — the R-rule says hold.
2. Skipping a setup because the dollar stop "feels too big" — the R-cap was set, the dollar is a derivation.
3. Sizing down below 1R because the dollar amount makes the trader nervous — collapses expectancy.
4. Recording the journal in dollar P&L instead of R — destroys the system's report card.