The QTM90800 automated trading fund manager used a method called Monte Carlo to test investment strategies. This method uses random sampling to predict possible outcomes. The manager tested 20,000 different paths, or possible market scenarios, using historical data from the last 12 months. The test assumed that 25% of the account equity would be used for each investment, starting with $850.99. The results are hypothetical and do not recommend actual investment allocations. For the SPY investment, the potential profit or loss was calculated to be between -$20.89 and $12.05, with a 68.97% chance of loss. For the EEM investment, the potential profit or loss was between -$13.74 and $26.39, with a 30.34% chance of loss. These results do not include additional costs or changes in market conditions. The manager also tested a single investment in SPY and EEM, with results ranging from -$4.19 to $4.74 for SPY and -$4.61 to $5.48 for EEM. The next exit from the investment is planned for October 5 or 6, but this is not guaranteed. The manager calculated the performance of the investments, with SPY showing a 47.46% profit and EEM showing a 59.32% profit. However, the actual profit was zero due to failed training and other factors. The manager also observed that the investments resulted in 28 stops, 8 targets, and 7 breakevens for SPY, and 21 stops, 21 targets, and 3 breakevens for EEM. The data used for these tests came from the website https://runway.adityaperswal.workers.dev/data/stock-bars, with observations from dates 9811, 9813, 9814, and 9815. A term called 'quarter Kelly' refers to a measure of investment performance. 'PDT' refers to a rule about pattern day trading. 'Whole-share rounding' refers to the practice of buying or selling whole shares of stock, rather than fractions of a share. '15min fills' refers to the time it takes to complete a trade, in this case 15 minutes.
A fund manager's analysis, called QIM90708, used a method called Monte Carlo to test investment sizes and 24-day scenarios with no investment, or zero exposure. The analysis looked at different investment options, including SPY and TLT, which are types of investments, and calculated their potential wins and losses. For SPY, the average win was 0.147373% and the average loss was 0.270744%. For TLT, the average win was 0.126794% and the average loss was 0.194806%. The analysis used 20,000 random 24-day periods from the past to test these investments. It found that the potential losses for SPY, EEM, EFA, and TLT investments were -7.2522%, -5.6375%, -5.6374%, and -4.5511%, respectively. The analysis also found that the frequency of losses was high, at 97.66% to 99.855%. The results showed that there were no gains in the sampled periods. The analysis concluded that it is best to protect cash and not invest to make up for the monthly shortfall. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the analysis used a seed of 90708 and a statistics program called stats.py.
A simulation of the QR290706 sizing and exit strategy found weak historical gains that cannot fund the shortfall. The simulation used the SPY stock, which is a type of stock that tracks the S&P 500 index, a measure of the US stock market. The results showed an average win of 1.197682% and an average loss of 0.879921%, which is a type of risk measurement. This implies a raw quarter-Kelly value of 0.605964% of equity, which is a way to measure the performance of an investment, and is approximately $5.16. However, the training Kelly value was 0, which means no allocation was made due to negative cost stress, a type of financial risk. The actual size was 0.2. The simulation ran 2,000 times over 60 months, with a hypothetical 25% account allocation per trade from $850.99. The results showed a terminal profit and loss of $-27.1884, $4.2175, and $35.0681, with 41.515% of the paths resulting in a loss. The 95th percentile monthly realized maximum drawdown, which is a measure of the largest loss, was 4.19024%. Separate 24-calendar-day realized cash flow windows produced a profit and loss of $-3.90996, $0, and $3.31432, with 21.82% of the windows resulting in a loss. The simulation also found 19 targets, 38 stops, and 7 RSI, which is a technical indicator used to measure stock performance, exits. The target frequency was 29.6875%, and the Wilson 95% confidence interval, which is a way to measure the reliability of the results, was [19.9050, 41.7704]. The simulation concluded that cash is the best option, and that no wider targets or risk increase should be made to address shutdown pressure. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the simulation was run using stats.py and recorded simulation figures.
A simulation was run to test the effectiveness of a trading strategy. The strategy's past trades had a win rate of 37.9310%, an average win of 2.59139%, and an average loss of 1.60982%. The simulation used 20,000 blocks of six months over 68 months, with no trading in inactive months. It assumed a 25% deployment of equity, starting with $850.99. The results showed a range of possible outcomes, with the median outcome being a loss of $2.61184 and the 95th percentile outcome being a gain of $42.27635. The simulation also found that 54.29% of the paths ended below the starting point, and the maximum drawdown was 5.52915%. A drawdown of more than 1% occurred in 99.48% of the paths. The simulation excluded certain factors, such as overhead costs and cash distributions. The results are based on historical data and should not be considered as forecasts. The strongest counterexample to the strategy's effectiveness is that some paths did show a profit, but this is not enough to create a reliable expected payoff. The simulation was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars and used observations 7818/7821 and stats.py.
A trading fund manager made some simulations to decide how much to invest in the stock market. The manager looked at the performance of the SPY stock, which is a way to invest in the US stock market as a whole, over 1,420 nights. The manager found that the probability of making a profit was 37.1127%, the average profit was 0.3810%, and the average loss was 0.4890%. However, the average return was -0.1661% after considering the costs of buying and selling, which is known as a 10bp per side cost, meaning a 0.1% cost for each transaction. This means that the manager should not invest in this stock, according to a formula called the quarter-Kelly allocation, which helps decide how much to invest based on the potential risks and rewards. The manager also did some other simulations, including one with 10,000 samples of five trades, and another with 100,000 paths of 25 trades, to see how the investment would perform over time. The results showed that the investment would likely lose money, with a median profit and loss of -$8.51, and only 6.961% of the simulations resulting in a profit. The manager also used some statistical models, including a Gaussian and a Student-t model, to simulate the performance of the investment, and found that the average return was around -0.1752%. The manager used some data from a website and some scripts to run the simulations, and the results were stored in some files called simulations.json, barriers.json, and scripts.
A news scan found the following information about a merger. The premises are as follows: the stock price of TXNM on September 4 was 58, with an offer of 61.25, resulting in a gross spread of 3.25 USD or 5.60345%. This calculation uses different break points, such as 40, 45, 50, and 55, and different time periods, such as 180, 270, and 365 days, with hurdle rates of 0, 4, and 6% and either no dividends or assumed continued dividends of 4.225 USD per quarter. At a break point of 50 and 270 days with a 4% annual alternative return, the required success probability is 87.1697% without dividends or 76.1309% with assumed dividends of 1.25014 USD. The cost of a win is 7.59741% and the cost of a loss is 11.89916% in the dividend case. A Bernoulli simulation with 100,000 paths per scenario and a seed of 90605 was run, and the results showed that the Kelly criterion, a measure of optimal bet size, is widely different for assumed success rates of 60, 75, and 90%. A wrapper simulation with 19 quarterly payoffs, 10000 paths, and a seed of 90604 showed a mean return of -0.32820% for MRGR and -0.26958% for MNA, with 95% confidence intervals of [-0.50152, -0.17937] and [-0.59914, 0.05320] respectively. The quarter Kelly was clipped to 0 for both. A hypothetical 25% fund allocation for 4 quarters showed terminal returns of -0.61930%, -0.31185%, and -0.03280% for MRGR and -0.88974%, -0.21603%, and 0.32215% for MNA at the 5, 50, and 95 percentiles respectively. These results cannot bound unseen crashes. The operator liquidations showed a mean return of -0.86097% and a Kelly criterion of 0, which are not natural strategy outcomes. The sources of this information are https://runway.adityaperswal.workers.dev/data/stock-bars, query_trades fundquant, https://www.txnmenergy.com/investors/acquisition.aspx, and https://www.prnewswire.com/news-releases/txnm-energy-board-declares-quarterly-common-stock-dividend-302699014.html.
The fund manager made some premises to guide their decisions. First, they looked at past data from the SPLV monthly cohorts, where they won 29.5455% of the time, with an average win of 1.32691% and an average loss of 1.10213%, resulting in a mean of -0.38446% after considering a 10 basis point one-way cost. They used a formula called the Quarter-Kelly, which resulted in 0. Second, they simulated 10,000 paths of 3-month blocks of data, with a 6-month horizon, and found that if they invested 12.5% of the fund's cash per monthly trade, the results would be a 5th percentile of -0.82239%, a median of -0.25872%, and a 95th percentile of 0.34792%, with a loss probability of 77.67% and a 95th monthly-marked drawdown of 0.82239%. Third, none of the simulated paths resulted in a gain of 349.01 on 245.69 fund cash, so the actual size remains 0. The simulation assumes that the payoffs will remain the same over time and does not account for unexpected jumps or real 15-minute stops. Another option, EEMV, had a positive quarter-Kelly of 0.6355%, but the results included losses and a 50 basis point stress loss of 29.04%, so it was not chosen. The fund manager also looked at three of their own liquidated trades, which had a probability of 33.33%, a win of 7.93994%, a loss of 5.26143%, and a mean of -0.860974%, resulting in a clipped quarter-Kelly of 0. This data came from a source at https://runway.adityaperswal.workers.dev/data/stock-bars, where they queried trades for the quant fund and saved the results in trades.json and simulation.json.
A news scan found the text: Premises: 1. The SPY range later history includes 44 calendar months, 19 trades, and a negative estimated expectancy. 2. Resample 44 monthly returns, including zero inactive months in circular 3-month blocks, 10,000 paths seed 906; 6-month horizon and hypothetical 12.5% fund allocation each entry, remaining cash return 0.3. Terminal fund return 5th/median/95th percentiles -0.59601/-0.070053/+0.560251%; 58.54% paths lose, 95th percentile monthly-marked DD 0.59601%. No simulated path earns 349.01 dollars on 245.69 fund cash. Conclusion: actual allocation 0 because quarter Kelly negative; 12.5% is a stress scenario, not recommendation. Distribution assumes stationarity and cannot represent unseen jumps, 15-minute exit delays, intraday DD, execution cost changes, or correlated fund drawdowns. Zero simulated target success does not mean true probability zero. Funding gap requires 142.052% of fund cash and 41.012% of account capital in 26 days, while experiment spans 6 months: wrong horizon and orders of magnitude to expect this rule to fund runway. Counterargument: positive unseen flow state could matter; must collect and validate it prospectively instead of leverage or relaxing stops. Source https://runway.adityaperswal.workers.dev/data/stock-bars; artifacts/stats.py frozen 906.
A 19-session cash-risk simulation was run to determine the optimal allocation. The simulation was based on historical daily portfolio outcomes, but it did not account for any potential future changes. The actual range of possible outcomes was 45.173% for the upper limit, 1.671877% for the first lower limit, and 1.522449% for the second lower limit, resulting in a negative binary expectancy and a clipped quarter Kelly of 0. The simulation used a NumPy seed of 2026090504, 10,000 paths, and circular 5-session blocks of net daily portfolio returns. The 19 sessions were not equivalent to 26 calendar days or a month-end forecast. The simulation assumed a daily constant fraction rebalance, ignoring additional rebalance costs, whole-share constraints, and intraday price movements. The results showed a range of terminal values, with the 5th percentile at -0.43042%, the median at -0.03387%, and the 95th percentile at +0.39688%. The loss frequency was 55.18%, and the maximum drawdown was 95.50076%. The fixed range was -0.58364% to +0.06186% to +0.65153%. The simulation also considered a nominal account stop risk ceiling of 8.5108 and a position cap of 212.77, but these were not guaranteed to be filled. The baseline fixed plug-in quarter Kelly of 81.42% was unstable and not authorized. The simulation was run using data from https://runway.adityaperswal.workers.dev/data/stock-bars and the stats.py and statistics.json files.
A nineteen-session runway simulation was conducted using NumPy stationary bootstrap with a seed of 90502, which sampled 10,000 paths of 19 trading sessions from each fixed strategy's net daily returns for 2023-Sep4, with a mean block of 20 sessions. This simulation represents a stylized near-month horizon and not an exact calendar or signal-conditioned forecast. The results showed that the range terminal returns were -2.77065%, +0.00468%, and +3.39568% for the 5th, 50th, and 95th percentiles, respectively, with a 40.63% loss probability and a 95th path drawdown of 3.66277%. A hypothetical full allocation of $245.69 would have resulted in returns of -$6.81, +$0.01, and +$8.34. The monthly version of the simulation showed returns of -6.49862%, +1.40311%, and +11.45624%, with a 38.07% loss probability and a 95th path drawdown of 11.37672%, resulting in dollar returns of -$15.97, +$3.45, and +$28.15. The actual book remains in cash. The simulation has limitations, including the potential for missing unseen tails and exact stop timing, and not calibrating the next entry. The range empirical p-value was 0.43609, with a W-value of 0.0249641 and an L-value of 0.0178879, resulting in a binary mean-payoff quarter-Kelly notional of 0.44756 and a monthly plug-in of 1.90339. However, these estimates are overconfident and not authorizations or risk fractions, and the range confidence spans zero and live exits remain unvalidated. The conservative eligible allocation is 0, with a maximum position of 212.77 and a nominal 1% equity of 8.5108, which remain ceilings and not targets. A funding need of $348.92 cannot alter these distributions. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the analysis was conducted using artifacts/stats.py and statistics analysis. The facts presented are based on measured historical paths, while the simulated probabilities are conditional model output.
A NumPy stationary bootstrap with a seed of 20260905 was used to analyze 10,000 paths of 27 trading sessions from the net 10-month portfolio daily returns from 2023-2026-09-04, with a mean block of 20 sessions. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The conditional returns for the 5th, 50th, and 95th percentiles were -2.7595%, +1.0247%, and +3.8952%, respectively. The probability of loss was 32.42%, and the 95th-percentile path maximum drawdown was 4.4290%. For a hypothetical full fund of $245.69, these returns imply -6.78, +2.52, and +9.57 dollars before operating overhead. None of the simulated paths resulted in a return of $348.92, but this does not prove that the true probability of this return is zero. The 27 sessions used in the simulation are longer than the 27 calendar days remaining and should not be used as a month-end forecast. The historical blocks used in the simulation omit unseen gaps and do not account for current holdings, which are actually cash. This model estimates variability based on resampled past exposure, not today's executable stop or target success. The conclusion is that funding pressure cannot justify sizing, and the simulation gives no live entry odds. A binary trade-payoff heuristic of quarter * (p - (1-p) / (avg win / avg loss)) = 0.0620 is a risk-fraction heuristic, but unequal continuous returns, overlapping trades, and unmodeled exits prevent translating it into dollar allocation. The Quarter-Kelly for the actual venue rule remains unavailable, and the policy size is 0. It is possible that a new trend could outperform all these paths, but there is no measured probability to support betting on it.