1. JJ Simon's Fair Pricing Theory and Prop Firm Strategy [0]

JJ Simon's strategy, Fair Pricing Theory, is a mechanical system that applies quantitative finance principles to trading, focusing on optimal risk and profit targets tailored to prop firm rules rather than market conditions.

1.1. Overview of JJ Simon's Trading Success and Core Philosophy [0]

  1. JJ Simon achieved over $1.8 million in payouts, starting with $5,000 and rapidly increasing his monthly earnings from $17,000 to $100,000 [0].
  2. His strategy involves no indicators and relies on pure price action, making it simple and mechanical [4].
  3. Simon's background in quantitative finance helps him apply a mechanical approach to charts [5].
  4. He emphasizes that a statistically optimal risk and take profit exists for every trade, determined by factors such as available drawdown, account rules (like consistency), and win rate [9].
    1. For example, a 1 to 4 risk-to-reward ratio requires a correct bias only 20% of the time to be profitable [13].
  5. Simon advocates for a high risk, high reward approach to effectively utilize the capital provided by prop firms, as low risk, low reward strategies are ineffective [15].

1.2. Fair Pricing Theory Explained [50]

  1. The strategy, called Fair Pricing Theory, is derived from quantitative finance principles [50].
  2. It aims to determine the fair price of NASDAQ futures, which Simon trades because it is primarily influenced by external factors like news and session opens [55].
  3. A session open (e.g., 9:30 a.m. EST New York session) creates an initial unfair move due to an influx of volume from overnight orders and institutional entries [59].
    1. The opening price of the 9:30 a.m. session is considered the initial fair price [65].
  4. The first trade of every session is a continuation trade in the direction of the opening candle, provided it aligns with a high-time-frame bias (reversion of the previous 6-12 hours) [67].
    1. The main entry signal is the opening candle's color, as long as it breaks the structure of previous candles [71].
    2. For this trade, Simon sets a 38-point take profit and a 25-point stop-loss [75].
    3. If the opening candle is larger than 25 points, he halves his contracts, effectively doubling the take profit to 76 points and the stop-loss to 50 points [76].
    4. This trade capitalizes on an unfair displacement [78].

1.3. Reversion Trades and Entry Signals [79]

  1. After the initial continuation trade, Simon looks for reversions to the opening price, which he considers the fair price [79].
  2. There are two primary entry signals for reversion trades:
    1. Displacement candle: The candle's body must be larger than the previous candle's body and close below its wick [84].
      1. This is a very mechanical entry, requiring only two checks [88].
      2. If a displacement occurs towards the fair price, Simon takes a short position [89].
    2. Break of structure: This is similar to a displacement entry but is slightly more profitable [101].
      1. A break of structure occurs when a wick is lower than the two surrounding candles, and a subsequent candle breaks that structure [108].
      2. Simon uses these intra-candle structures for easy, mechanical entries [753].
  3. All entries are based on the one-minute time frame, except for the high-time-frame bias, which can be observed on the five-minute chart or by looking at the one-minute chart for the previous 12 hours [90].

2. Prop Firm Optimization and Risk Management [107]

JJ Simon optimizes his trading strategy for prop firms by adjusting risk and profit targets based on specific account rules, aiming to maximize expected value and manage multiple accounts efficiently.

2.1. Adapting Strategy to Prop Firm Rules [107]

  1. Simon selects different prop trading accounts based on their rules, as some are better suited for 100-point take profits, others for 50, and some for 25 [116].
  2. He uses static risk and static take profits for most trades [123].
    1. For evaluations, a 1 to 1.5 risk-reward ratio (e.g., $2,000 max loss, $3,000 profit target) is optimal because it aligns with the evaluation's drawdown trailing rules, leading to a higher pass rate [125].
    2. Evaluations include all displacement candle entries [139].
  3. For funded accounts, Simon prefers break of structure entries because they are stronger [140].
    1. He takes 100-point trades on funded accounts that do not have a consistency rule to realize larger wins and maximize expected value [130].
    2. If a prop firm moves traders to a live account with reduced simulated capital, Simon avoids large wins on funded accounts to ensure the live account is substantial enough to be worth trading [132].
  4. Simon manages multiple accounts by spreading risk and taking as many trades as possible with a slight bias towards a fair price [103].

2.2. Consistency Rules and News Events [157]

  1. For firms with a consistency rule (e.g., 50% consistency), Simon limits his winners by using a 1 to 1.5 risk-to-reward ratio (sometimes 1 to 2) [158].
    1. Winning too often can increase the profit target, making it harder to achieve payouts [160].
    2. Lower risk-to-reward ratios help reach the profit goal faster on consistency accounts [611].
  2. Fair Pricing Theory is heavily influenced by news events [169].
    1. The market open is considered an event due to the influx of volume creating an initial unfair move [170].
    2. Simon trades during the first 90 minutes of every session (New York, Asia, New York PM) [174].
  3. There are two types of news:
    1. Expected news (e.g., 8:30 a.m. Red Folder news like CPI, PPI) is usually priced in, meaning the market has already moved to a fair price [188].
      1. Simon's favorite trade is the expected news reversion, where an initial large candle move is considered extremely unfair [192].
      2. He uses the same entry model (displacement or break of structure) to trade reversions back to the pre-news fair price [201].
    2. Unexpected news (e.g., tweets, speeches) changes the fair price of futures [206].
      1. After unexpected news, the most recent consolidation becomes the new fair price [213].
      2. Two types of trades are possible with unexpected news:
        1. Continuation (news drift): Entering immediately after a huge jump in the chart to capitalize on a slight positive expectancy for the price to continue in the news's direction [217].
        2. Reversion: Trading back to the most recent consolidation after the initial news drift [225].

3. Continuation Trades and Stop-Loss Strategy [228]

Continuation trades, especially after unexpected news, are managed with larger stop-losses and are typically reserved for funded accounts due to their less predictable profit targets compared to reversion trades.

3.1. Managing Continuation Trades [228]

  1. Continuation trades are generally less favorable than reversions because the exact fair price is unknown [231].
    1. For reversions, the target is a fixed point (e.g., market open price) [233].
  2. For continuations, Simon uses the largest possible stop-loss [235].
    1. This allows the trade to withstand random price fluctuations while still realizing a slight positive bias [237].
  3. Continuation trades are primarily taken on funded accounts [241].
    1. The small percentage of edge in these trades is more valuable to capture on funded accounts, which generate payouts, rather than on evaluations [241].

3.2. Stop-Loss and Exit Strategy for Continuations [245]

  1. The stop-loss for continuation trades is typically placed below the previous consolidation [245].
  2. Simon does not set a fixed profit target for continuations because the price discovery area is unpredictable [249].
    1. Instead, he manually monitors the trade and exits if the price breaks structure against his position [251].
    2. He always waits for a candle close to confirm a break of structure or displacement before exiting [257].
  3. Simon's strategy is characterized by no indicators and pure price action, making it simple and effective [263].

4. Building a Mechanical Trading System and Backtesting [266]

JJ Simon's approach to trading is rooted in a deep understanding of mathematics, statistics, and finance, which he uses to build a mechanical system optimized for prop firm environments through specific backtesting methods.

4.1. Developing the Mechanical Approach [266]

  1. Simon's background in quant finance, math, and statistics was crucial in developing his mechanical trading approach [266].
  2. For other traders, the most important step is to find the optimal risk for prop firms [269].
  3. This optimal risk and take profit for each trade is based on:
    1. The available drawdown [270].
    2. The account rules (e.g., consistency, winning days) [271].
    3. The win rate (risk-to-reward ratio and frequency of wins) [271].
  4. A high win rate (small risk-to-reward, e.g., 1:1) allows for more risk, while a low win rate (e.g., 1:6) requires less risk [272].

4.2. Backtesting for Prop Firm Success [310]

  1. Simon emphasizes that traders should backtest in a prop firm environment, not just for live account equity curves [310].
    1. Prop firms are designed such that strategies profitable on live accounts may not work due to their specific rules [314].
  2. The goal of backtesting for prop firms is to determine the pass rate, not the equity curve [319].
    1. To do this, simulate trading with multiple accounts (e.g., 10 accounts) using the same risk-to-reward ratio on evaluations [316].
  3. A formula to calculate the cost of a funded account is: Cost of Evaluation / Pass Rate [323].
    1. For example, if an evaluation costs $100 and the pass rate is 25%, it costs $400 to achieve a funded account [326].
  4. Once the average cost of a funded account is known, ensure the expected value of the funded account is greater than this cost to confirm profitability [330].
    1. The expected value of a funded account is the total amount that can be taken out in payouts over its lifetime [332].
  5. Backtest different static risk-to-reward ratios on funded accounts to find the optimal one that maximizes payouts [340].

5. Limitations and Adaptations of the Prop Firm Model [350]

The prop firm trading model has limitations, particularly regarding variance and evolving firm rules, requiring traders to adapt their strategies and understand how these changes impact expected value.

5.1. Addressing Variance and Evolving Rules [350]

  1. Variance (luck) is a significant factor, so a large sample size (e.g., 50 evaluations) is needed for backtesting to get a reliable estimate of the pass rate [353].
  2. Prop firm rules are constantly changing, with live accounts being introduced more frequently [364].
    1. These changes can affect the expected value of a strategy [366].
  3. Traders must understand how moving to a live account impacts their expected value [368].
    1. The expected value of a live account is generally its balance (e.g., $10,000 for a $10,000 account), assuming a break-even trader [370].
    2. If a large simulation balance is accumulated, traders should account for the likely reduction to a smaller live account in their expected value calculations [378].
  4. Simon's specific profit target and risk-reward models are prop firm-specific [380].
    1. On a personal live account, he would trade only reversions, specifically break of structure and expected news reversions, and use a discretionary approach rather than static risk and take profit [382].
  5. Expected news reversions are considered the strongest setup because news is almost always priced in, making the initial move unfair [389].
    1. In contrast, the market open is not always a fair price, making news reversions more reliable [397].

5.2. Common Struggles and Adaptations [411]

  1. A common mistake traders make is backtesting Simon's strategy on a live equity curve instead of a prop firm-specific environment [418].
  2. Another struggle is adapting to the fair price [429].
    1. If trades are consistently losing, it indicates the market is trending heavily, and the initial fair price (e.g., the open) may no longer be valid [431].
    2. This requires a slight discretionary adjustment to the strategy [436].
  3. Simon emphasizes using the bias of fair price on prop firms, even if entry models or confluences differ [440].
  4. To manage discipline and avoid overtrading:
    1. Simon stops trading a reversion strategy if he incurs three consecutive losses in a session, indicating the market is not reverting as expected [451].
    2. He views "tilting" (random trading) as introducing unneeded variance rather than being inherently bad, as it has a break-even expected value [455].
    3. If feeling inclined to tilt, Simon advises trading on evaluation accounts instead of funded accounts [465].
      1. This spreads risk across multiple evaluations, reducing variance and capping potential losses to the cost of the evaluations [472].

6. Managing Multiple Accounts and Trade Examples [478]

JJ Simon manages numerous prop firm accounts by layering trades based on market conditions and account-specific optimizations, demonstrating his strategy through detailed examples of news and session open trades.

6.1. Strategy for Managing Multiple Accounts [478]

  1. Simon trades during specific sessions (e.g., 9:30 a.m. to 11:00 a.m. New York session) and aims to take 30 trades throughout the day [479].
  2. He identifies the fair price and trades towards and from it throughout the session, utilizing multiple entry opportunities like displacement [487].
  3. Simon uses Trade of Eight to open multiple tabs, allowing him to place trades across different prop firms simultaneously [846].
  4. He layers evaluations by entering one account early on displacement and then another on a break of structure, maintaining the same exposure but reducing variance [516].
  5. Account selection is based on the optimal risk-to-reward ratio and specific rules of each prop firm [497].
    1. For evaluations, he consistently uses a 38-point take profit and 25-point stop-loss (38/25) regardless of the potential points to fair price, as this is optimal for his static risk-to-reward [526].

6.2. News Event Trade Example (CPI Day) [732]

  1. On a CPI day (expected news), the price before the 8:30 a.m. news is considered the fair price [732].
  2. Simon looks for short entries back towards this fair price, waiting for a displacement candle or a break of structure [740].
    1. The first entry was a break of structure [746].
    2. The target was the fair price, which was 175 points away [748].
  3. Take profit is optimized first based on the prop firm's rules, not market conditions [780].
    1. Simon's dashboard indicates the optimal profit target for each account (e.g., 175 points) [782].
    2. The stop-loss is then determined by the account's risk limit (e.g., $500 risk equals a 25-point stop-loss with one contract) [786].
  4. Simon acknowledges that price can hit the stop-loss even if the bias remains intact, but with 45 accounts, he is ready to re-enter [24].
  5. He layers in more trades on different accounts as the price moves, capitalizing on the bias [807].
    1. An example shows a strong breakout structure with 94 points in his favor, suitable for a 100-point target on a funded account [810].
    2. He also takes evaluation trades (38/25) if he feels he has too much exposure on funded accounts [851].
  6. When trading news, the pre-news price is stronger than the open as a fair price, especially if the market opens and immediately reverts to it [875].

6.3. News Event Trade Example (PPI Day) [979]

  1. On a PPI day, Simon focuses on reverting to the pre-news price [980].
  2. He looks for break of structure as the first short entry [991].
    1. A displacement candle must displace a candle of the opposite color [996].
  3. Simon's stop-loss is never less than 25 points in the New York session [1009].
  4. He uses contract sizing to adjust for different take profit points while maintaining the same dollar amount of risk and reward [936].
    1. For example, if an opening candle is 43 points (more than 25), he halves his contracts to maintain the 50-point stop loss and 76-point take profit dollar values [964].
  5. Simon layers in multiple evaluation trades in the direction of his bias [954].
  6. He prioritizes getting through as many accounts as possible with the 38/25 setup [960].

7. Financial Journey and Future Goals [658]

JJ Simon's journey highlights the significant payouts possible with prop firms, the costs involved, and his strategic evolution towards optimizing for live accounts and eventually managing his own hedge fund.

7.1. Payouts, Costs, and Market Changes [658]

  1. Simon has achieved $1.5 million in payouts over 18 months [658].
  2. The cost to achieve this was approximately $400,000 [659].
  3. His monthly payouts initially grew rapidly (e.g., $17,000, then $20,000, $40,000, $60,000, $80,000, $100,000) [663].
  4. Payouts are now slightly lower (e.g., $100,000, then $75,000, $60,000, $100,000) due to prop firms increasing prices and moving traders to live accounts more frequently [666].
  5. New traders can still achieve several six-figure months before prop firms limit them or move them to live accounts [670].

7.2. Optimizing for Live Accounts and New Firms [675]

  1. Simon's current focus is on optimizing his approach around live accounts [632].
    1. Prop firms now move traders to live accounts more often, which requires understanding how this impacts expected value [635].
    2. He aims to go live only when it is profitable, keeping his simulated funded balance below the expected value of the live account (e.g., below $15,000 for a $15,000 live account) [638].
  2. Simon enjoys trading with new prop firms because they often have good payout policies and must pay out to build their reputation [648].
    1. He starts with one account, gets a payout, reinvests it, and then runs five funded accounts for maximum payouts [653].
  3. His long-term goal is to reach a $5 million net worth and open a hedge fund [684].
  4. He plans to trade prop firm-specific live accounts by optimizing for bonuses [694].
    1. For example, if a $150K live account offers a $4,500 bonus for making $4,500, he will stop trading once he hits $4,500 to secure the bonus, as risking it further means risking the bonus as well [695].

8. Starting Capital and Risk of Ruin [1073]

Understanding the necessary starting capital and calculating the risk of ruin are crucial for traders adopting a high-risk, high-reward strategy in the prop firm environment.

8.1. Recommended Starting Capital and Risk Approach [1073]

  1. Simon was fortunate to start with $5,000, which he earned from playing poker, providing him with a larger bankroll and a very small risk of ruin [1073].
  2. He believes a high risk, high reward approach is the only way to make money with prop firms, as low risk, low reward strategies are ineffective [1075].

8.2. Calculating Risk of Ruin [1078]

  1. Traders need to calculate their risk of ruin and aim for it to be below 5% [1078].
  2. The risk of ruin can be calculated by multiplying the win rate (e.g., 30-33%) by the payout rate (chance of getting a payout on a funded account) [1080].
    1. For example, if buying an evaluation gives a 0.09% chance of payout, it means there's a 91% chance of getting nothing [1082].
    2. Although there's a high chance of losing the $100 evaluation fee, the eventual payout (e.g., $2,000 or more) makes it profitable in the long run [1084].
  3. To calculate the chance of losing everything with a given bankroll (e.g., $1,000 for 10 evaluations), raise the probability of not getting a payout (e.g., 0.91) to the power of the number of evaluations (e.g., 10) [1088].