The automated trading fund manager made notes about the EEM investment. The manager started with three premises. First, the manager used a protocol with rules 596 and 598, which were set before prices were considered, and looked at a measure called RSI2, which had declined for three days starting from below 65, and the close was higher than the SMA200, a moving average of 200 days. The manager then decided to enter or exit the investment at the next open, after the RSI went above 75, without any overlap. Second, the manager looked at recent samples from 2016 to 2020 for training and from 2021 to 2025 as a holdout, and from 2026 as a recent sample, without adjusting any parameters, but had previously inspected the market period. Third, the manager used a long-only strategy with a 5 basis point fee and a 5 basis point slip in each direction, which was reconciled with independent wealth, and marked daily closes as DD, with adjusted prices that approximated cash distributions. The results showed a mean of 1.7633368092934865% and a worst result of -0.9457374891720383%. The manager concluded that this was only a baseline and did not validate stop, target, or trail strategies, and that a profitable holdout period did not make up for earlier negative periods or justify entering the investment. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected to maximize profit, and all five ETFs were retained.
This report is based on certain premises. The first premise is that the Protocol 596/598 is frozen before prices are considered, and the Relative Strength Index (a measure of stock price movement) declines for 3 days starting below 65, with the close being greater than the Simple Moving Average (a trend line) of 200. The entry and exit points are determined by the next open after the Relative Strength Index is greater than 75, with no overlap. The second premise is that there is a holdout chronological sample, where the data from 2016 to 2020 is used for training, 2021 to 2025 is used for holdout, and 2026 is considered recent. No parameters are fitted, but the market period has been previously inspected. The third premise is that there is an explicit cash long only vector with a 5 basis point fee and a 5 basis point slip each way, which reconciles independent wealth. The daily close is marked and adjusted prices approximate cash distributions. The results show a mean of 0.01995432325801159% and a worst-case scenario of -6.621975820217618%. The conclusion is that this is only a baseline and does not validate stop, target, or trail. A profitable holdout does not make up for a negative earlier period or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No sample or parameter was selected for profit, and all five ETFs were retained.
A news scan found the headline about a trading strategy. Premises for this strategy are as follows: the protocol, which is a set of rules, was frozen before prices were considered, and it uses a measure called RSI2, or Relative Strength Index, which declines for 3 days starting below 65, and the close is greater than the SMA200, which is a moving average. The strategy also uses an open entry and exit after the RSI is greater than 75, with no overlap. The training sample is in chronological order, with the years 2016-2020 used for training and 2021-2025 used for holdout, and 2026 is recent. There are no fitted parameters, but the market period was previously inspected. The strategy uses a cash long only vector, which means it only buys, with a 5 basis point fee and a 5 basis point slip each way, and it reconciles independent wealth. Daily close is marked as DD, and adjusted prices approximate cash distributions. The results show a mean of 0.10842871778365562% and a worst of -5.963594231989811%. The conclusion is that this is only a baseline, and it does not validate stop, target, or trail. A profitable holdout does not repair a negative earlier period or justify entry. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars, and the original source is https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No sample or parameter was selected for profit, and all five ETFs, including EEM, were retained.
The automated trading fund manager made the following premises: the Protocol 596/598 was frozen before prices were considered, and the Relative Strength Index (a measure of stock price movement) 2 declines for 3 days starting below 65, with the close being greater than the Simple Moving Average (a stock price average) 200. The manager also considered the recent chronological sample, which includes data from 2016 to 2020 for training and 2021 to 2025 for holdout, and 2026 for recent data, with no parameters fitted, but the market period was previously inspected. The manager used an explicit cash long only vector with a 5 basis point fee and 5 basis point slip each way, which reconciles independent wealth, with daily close marked and adjusted prices approximating cash distributions. The results showed a mean of -0.4898687579872285% and a worst of -2.445854225070998%. The conclusion is that this is only a baseline and does not validate stop, target, or trail, and a profitable holdout does not repair a negative earlier period or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No sample or parameter was selected for profit, and all five ETFs were retained.
The following premises were used: the Protocol 596/598 was frozen before prices were considered, and the Relative Strength Index (a measure of stock price movement) 2 declines for 3 days starting below 65, with the close being greater than the Simple Moving Average (a stock price average) 200. The entry and exit points were determined by the following open after the Relative Strength Index was greater than 75, with no overlap. A chronological sample was used as a holdout, with the years 2016-2020 used for training and 2021-2025 used as a holdout, and 2026 considered recent. No parameters were fitted, but the market period was previously inspected. An explicit cash long only vector was used, with a 5 basis point fee and a 5 basis point slip each way, reconciling independent wealth. Daily close was marked to the decimal day, and adjusted prices approximated cash distributions. The results showed a mean of 0.07524363740623041% and a worst loss of -8.519103138748662%. The conclusion is that this is only a baseline, and does not validate stop, target, or trail strategies. A profitable holdout does not make up for a negative earlier period or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected for profit, and all five ETFs were retained.
A trading plan for EFA has been made. It is based on three premises. First, the plan uses a method called Protocol 596/598, which is frozen before prices are considered, and it looks at something called the RSI2, which is a measure of how fast prices are changing, and it must have been declining for 3 days and be below 65, and the closing price must be higher than the SMA200, which is the average price over 200 days. The plan also looks at when to enter and exit the market after the RSI is higher than 75, with no overlap. Second, the plan uses a sample of data from 2016 to 2020 for training and from 2021 to 2025 for testing, with 2026 being the most recent data, and it does not use any parameters that are fitted to the data, but it does look at the market period beforehand. Third, the plan uses a long-only strategy, which means it only buys, and it uses a fee of 5 basis points and a slip of 5 basis points each way, and it reconciles the wealth independently and marks the daily close as DD, and it adjusts the prices to approximate the cash distributions. The results show a mean of -0.10215037465943766% and a worst result of -8.944736735083858%. The conclusion is that this is only a baseline and does not validate stop, target, or trail, and a profitable test period does not make up for a negative earlier period or justify entering the market. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No sample or parameter was selected to make a profit, and all five ETFs were kept.
The automated trading fund manager made the following premises: the protocol 596/598 was frozen before prices were considered, and the Relative Strength Index (a measure of stock price movement) 2 declines for 3 days starting below 65, with the close price being greater than the Simple Moving Average (a measure of stock price trend) 200. The manager also considered the recent chronological sample, which includes data from 2016 to 2020 for training and 2021 to 2025 for holdout, and 2026 for recent data, with no fitted parameters but previously inspected market periods. The manager used an explicit cash long only vector with a 5 basis point fee and 5 basis point slip each way, which reconciles independent wealth, and daily close was marked as DD, with adjusted prices approximating cash distributions. The results showed a mean of 1.272982569226051% and a worst loss of -0.05446169379021981%. The conclusion is that this is a baseline only and does not include stop, target, or trail validation, and a profitable holdout period does not make up for a negative earlier period or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No sample or parameter was selected for profit, and all five ETFs were retained.
This report is based on premises that include a protocol with specific rules. The rules are: the Relative Strength Index, or RSI, a measure of how quickly a stock's price is changing, declines for 3 days starting below 65, and the stock's closing price is higher than its Simple Moving Average, or SMA, over 200 days. The report also includes a holdout, which is a sample of data set aside to test the protocol, from the years 2021 to 2025. The data is adjusted to account for cash distributions and fees. The results show a mean return of 0.9381751690784071% and a worst return of -6.804647419897636%. The conclusion is that the holdout period was profitable, but this does not make up for earlier losses or justify entering the market. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were chosen to maximize profit, and all five ETFs were kept.
This is a description of a trading strategy for the IWM fund. The strategy is based on three premises: first, certain rules are applied before prices are considered, including a decline in the RSI2 indicator for three days starting below 65, and the closing price being above the SMA200 indicator; second, the strategy is tested on a sample of data from 2016 to 2020, with a holdout period from 2021 to 2025, and a recent period in 2026; third, the strategy uses a cash long only approach with a 5 basis point fee and a 5 basis point slip each way. The strategy was tested on 39 samples, with a mean return of -0.6433249944791336% and a worst return of -30.15412515129472%. The conclusion is that this is only a baseline strategy and does not include stop, target, or trail validation. The strategy was profitable in the holdout period, but this does not make up for negative returns in earlier periods or justify entering the trade. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars and is based on an article from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected to maximize profit, and all five ETFs were retained.
A trading strategy for the SPY stock has been analyzed. The strategy is based on three premises: first, a protocol that freezes prices before they are used, and looks for declines in the RSI2 indicator over three days, starting below 65, and a close above the SMA200 indicator, with entry and exit rules based on RSI levels above 75, with no overlap. Second, the strategy uses a recent chronological sample of data from 2016 to 2020 for training and 2021 to 2025 for holdout, with 2026 being the recent period, and no parameters were fitted to the data, but the market period was previously inspected. Third, the strategy uses a cash long only approach with a 5 basis point fee and 5 basis point slip each way, and reconciles independent wealth, with daily close marked and adjusted prices approximating cash distributions. The results show a mean return of 0.8809568246536165% and a worst return of -1.797301171955723% over 11 periods. The conclusion is that this is only a baseline strategy and does not include stop, target, or trail validation, and a profitable holdout period does not make up for negative earlier periods or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected for profit, and all five ETFs were retained.
A trading strategy for the SPY stock has been analyzed. The strategy is based on three premises: first, a set of rules including a decline in the RSI2 indicator over three days starting below 65, and the stock's close price being above its SMA200, with entry and exit points determined by the RSI indicator crossing 75, with no overlap. Second, the strategy uses a holdout sample, which is a chronological sample of data from 2016 to 2020 for training, 2021 to 2025 for holdout, and 2026 for recent data, with no parameters fitted to the data, but the market period was previously inspected. Third, the strategy uses a cash long only vector with a 5 basis point fee and 5 basis point slip each way, which reconciles independent wealth, with daily close marked and adjusted prices approximating cash distributions. The results show a mean return of 0.7162919775054395% and a worst return of -5.606497085752837% over 39 periods. The conclusion is that this is only a baseline test, not a validation of stop, target, or trail strategies, and a profitable holdout period does not make up for negative earlier periods or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected to maximize profit, and all five ETFs were retained.
A trading strategy for the SPY fund is based on three premises. First, the strategy uses a protocol with specific rules: it looks at the Relative Strength Index, or RSI, which measures how quickly a stock's price is changing, and only considers trades when the RSI is below 65 and the closing price is higher than the Simple Moving Average, or SMA, of the last 200 days. It also considers trades after the RSI goes above 75, without overlapping trades. Second, the strategy uses a chronological sample of data from 2016 to 2020 for training and from 2021 to 2025 for testing, without adjusting any parameters. Third, the strategy uses a cash long only approach, which means it only buys stocks, with a 5 basis point fee and a 5 basis point slip, and reconciles independent wealth. The strategy was tested on the SPY fund with 39 trades, resulting in a mean return of -0.4071933868780629% and a worst return of -22.67570559906531%. The conclusion is that this strategy is only a baseline and does not validate stop, target, or trail rules. The strategy was profitable in the test period but did not make up for earlier losses. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected to maximize profit, and all five ETFs were retained.
The QR290706 ETF has certain economic characteristics. The premises are as follows: 1. The issuer's net asset value on September 4 was 108.09, and the current annual expense is 0.32%, which is made up of 0.31% management fee and 0.01% other expenses. 2. The EFA owns developed large and mid-cap equities outside of the US and Canada, and daily ETF returns already reflect wrapper expenses, which refers to the costs associated with the ETF structure. 3. The approximate 10 calendar-day expense of 0.00877% is small compared to the assumed 20 basis point round trip, but cumulative fees and turnover remain a real consideration. In conclusion, one should not double charge fund expenses in the backtest or call oversold based on RSI fundamental undervaluation, as RSI stands for Relative Strength Index, a measure of stock price movement. Underlying earnings and foreign exchange, as well as discount to market value, are not estimated by RSI. A counterexample is that temporary price concession can exist without cheap underlying businesses, and validated payoff is still needed. The manager collects fees regardless of our alpha, which refers to the excess return of the fund, and brokers collect turnover costs. No issuer-specific earnings forecast or net asset arbitrage is proposed. This information is based on a data source from https://www.ishares.com/us/products/239623/ishares-msci-eafe-etf, with observation number 8019.
A report, QR290706, has been published with evidence of reversals. The report is based on several premises. First, an article by Connors from February 2007 reported an 84.31% win rate out of 102 SPX trades from 1995 to 2006 using a day 3 same close RSI 2 entry, which is a technical indicator that measures the speed and change of price movements, specifically the Relative Strength Index with a 2-day period. However, this strategy is not executable after the close of trading is completed. Second, a study by Frazzini, Israel, and Moskowitz from December 2012 examined the prior-month stock cross-section, large manager execution, and non-zero STR capacity, which refers to the ability to execute trades without significantly affecting the market price. The study found that not all small reversals fail. Third, the use of a cash-only ETF next open/barrier adaptation is a new implementation and not an exact replication of institutional strategies. The conclusion is to freeze separate testing rather than importing either the old win rate or blanket rejection. The validity of the report is based on different populations, timing, and costs, and while the original author's rules have been verified, the 2007 simulation is unaudited and the AQR historical cost extrapolation assumes stable coefficients. A counterexample is that small orders can avoid capacity impact yet still pay spreads and can lose due to overshooting stops. The prior remains an unknown net edge, but positive independent actual exit validation would change allocation. The sources of the information include a recent article on the improved R2 strategy and a study on the trading cost of asset pricing anomalies. The protocol numbers are 595, 596, and 598. The international paper data sample is from 1983 to 2011, with an internal inconsistency on page 27 heading 1982, which is not part of the data used.
The QEX90704 swing adaptation has been rejected and the quant cash will be retained. The decision is based on the following premises: 1. The original DBC monthly result showed a return of +104.6964% over 42 later cohorts, with the main swing gate admitting 29, whose matched monthly hold returns were +37.3643%. A swing gate is a mechanism that controls the flow of trades. 2. The actual frozen proxy exits returned -1.1431%, with a drawdown of 10.0758%, a profit factor of 37.9310%, an average win of 2.59139%, an average loss of 1.60982%, and a quarter Kelly of 0. Kelly is a measure of risk. 3. DBC was selected due to its strong known result, and its price-bar proxy lacks executable quote, poll, and overnight history, as well as exact distribution cash. Only two main cohorts began after the method change in November 2025. 4. The September monthly formation passed, with no catch-up permitted, and the account is flat, with $245.69 in quant cash and no pending orders. The conclusion is that there are no proposals, and the QEX90704 swing adaptation is rejected. The monthly benchmark will be preserved as research WATCH only. The strongest counterargument is the lower drawdown and profitable 2022/narrow low-cost variant, but negative cost stress and wide uncertainty prevent adoption. The goal is to validate the net edge, with a binding constraint payoff validation, not capital. The facts end at historical observations, and the future edge is unknown. The next research will choose a new prespecified question instead of repeated same-window exit tuning. A news scan found the headline is not applicable here, this is a detailed report. Analyses 498, 510-515, 525-528, and observations 7787-7821 were used. The code archive 557-562 readback was verified, and the index is 564. The sources used include https://runway.adityaperswal.workers.dev/data/stock-bars, https://pages.stern.nyu.edu/~lpederse/papers/TimeSeriesMomentum.pdf, https://www.sec.gov/Archives/edgar/data/1328237/000119312526083563/dbc-20251231.htm, and https://docs.alpaca.markets/us/docs/market-data-faq.
The QEX90704 fund started with $245.69 in cash and no holdings, aiming for a repeatable net payoff. The fund's rule for exiting trades was validated. The fund's premises were based on selecting entries with a 12-month excess trend, which resulted in a 37.3643% return versus a -1.1431% swing return, with a median hold of 30 days versus 1.03125 days. The first order of the fund was to use range targets and trailing exits to shorten exposure and realize small gains and stop gaps. The second order was that less participation in sustained commodity moves and greater cost weight per unit of captured movement resulted in doubled transaction costs, lowering the swing return to -6.7137%. The third order was that reusing the monthly 59.52% win rate or 8.55% quarter Kelly for this altered payoff mis-sizes a rule whose observed p37.93% yields zero quarter Kelly. Manager asset fees and trading intermediaries' turnover revenue persist regardless of the fund's alpha. A 2020 AQR primary author summary identifies move size as a driver of trend returns, but this is mechanism context, not independent proof that targets caused the result. The paired confidence interval includes zero and only one selected wrapper was studied, so no universal causal conclusion can be made. The strongest counterargument is that shorter exposure lowers sampled drawdown, with a matched monthly 26.47% versus swing 10.08%, and the 2022 swing profited 12.99%. Lower risk is useful but cannot substitute for positive net expectancy. Therefore, the fund will retain cash and not compensate for the $349.01 shortfall with leverage, wider targets, or a selected variant. Sources include research from AQR, stock bars data, and an SEC filing.
On September 4, the stock closed at $31.90, which is above its 50-day moving average of $29.3594 and its 200-day moving average of $27.2321. The Average True Range, a measure of volatility, is $0.42093 over 20 days. The Average Daily Range, another measure of volatility, is $0.321705 over 20 days. Using these numbers, a possible stop-loss price would be $31.47907 and a target price would be $32.531395, with a trailing stop of 1.31953% and a target based on 1.96265 times the Average Daily Range. These levels are based on the last 20 days of trading and are not actual orders. Buying six shares would cost $191.40, with a potential loss of $2.52558, which is below the position cap of $212.7475 and the account risk cap of 8.5099%. The average daily trading volume over the last 20 sessions is $17.2938 million, which suggests that the stock may not be able to handle large orders. As of August 12, the stock had returned 45.64914% over the last month, compared to 3.71127% for a benchmark, with an excess return of 41.93787 percentage points, or a relative wealth gain of 40.43714%. The stock's positive trend is not a new development, and the potential for a new entry point is not until October. Therefore, no new entry will be made, with an empirical swing Kelly ratio of 0 and an actual quantity of 0. Note that even though the stock is affordable and trending upward, these factors do not necessarily mean that it will be profitable. The price data comes from https://runway.adityaperswal.workers.dev/data/stock-bars, and the bar data semantics come from https://docs.alpaca.markets/us/docs/market-data-faq. Stop-loss orders sampled every 15 minutes can still result in losses beyond the nominal risk, with a historical median overshoot of 0.1291 times the risk and a maximum overshoot of 0.9306 times the risk.
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 fund manager analyzed the QEX90704 trading strategy, which is a set of rules for buying and selling stocks. The analysis was based on three premises. First, the strategy was tested on a specific sample of data from January 2021 to August 2026, which included 29 active cohorts within 68 calendar months. Second, the manager resampled 20,000 six-calendar-month blocks with a seed of 90704, retaining inactive months, and divided payoff sums by active counts. The mean trade was -0.016256%, with a 95% interval of -0.765399% to +0.793882%. The positive resample fraction was 0.46225, but this is not a measure of the true edge of the strategy. Third, a comparison between monthly and swing trades showed a mean difference of -1.228586 percentage points, with a 95% interval of -2.884591 to +0.429574, indicating that the population difference is not established at 95%. The correlation between monthly and swing returns was 0.484933, and the beta of swing returns on monthly returns was 0.221734, with a standard error of 0.055223 and an intercept of -0.285071%. Removing the three best matched monthly trades reduced the total return from 37.3643% to 7.29442%. The main swing strategy excludes 2022 and has only two eligible cohorts after the methodology change in November 2025. The conclusion is to reject the adoption of the QEX90704 strategy due to the lack of demonstrated positive payoff. This decision may change with a preregistered independent sample showing a positive net lower confidence bound and executable 15-minute fills. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with observations 7817, 7820, and 7821, and scripts stats.py and audit.py.
A test of the QEX90704 system found that it did not work well. The test used frozen diagnostic variants and did not choose a winner. The main test had a return of -1.1431% at 10 basis points per side and -6.7137% at 20 basis points. When the next available bar-open fill was used after a signal, the return got worse to -5.6795% and -10.9945%. When only regular-hours-only closes were used, the return was -6.2437% and -11.5269%. A variant called Narrow0.75ATR allowed 42 later cohorts and had a return of +2.84395% at 10 basis points, but -5.44207% at 20 basis points, and -7.24753% and -11.24016% in training. This variant changed both admissions and exits, so it was not possible to isolate the effect of stop-distance. A variant called Wider1.25ATR did not allow any cohorts because the target exceeded 2 average daily ranges, so no return estimate was possible. All 28 rows of variant, split, and cost data had observations from 7787 to 7814, from the source https://runway.adityaperswal.workers.dev/data/stock-bars. The test was done with parameters that were set before the outcomes were known, but the underlying window and winner had already been looked at. The test was to see if the system was robust, not to select a new system. The conclusion is to reject using the swing system live. The strongest argument against this conclusion is the positive result from the narrow low-cost variant, but it was defeated by the sensitivity to cost, negative training, and uncertainty after selection. This test does not claim that all possible exits will fail. The main sample used same-day exits, which means that any future use must coordinate with the account-wide 3-in-5-day limit. There are no orders now.
A news scan found the notes of an automated trading fund manager. The manager tested a trading strategy on the DBC fund. The strategy had a 12-month adjusted return that was better than the BIL fund. The manager set a stop loss at 1 times the average true range and a target at 1.5 times the average true range. The manager also set a trailing distance of 1 times the average true range. The strategy was tested from 2017 to 2020 and then from 2021 to August 2026. The manager assumed a fee of 20 basis points per side, which is a measure of the cost of trading. The results showed a return of -6.71%, a drawdown of 12.66%, and a win rate of 37.93%. The manager concluded that this strategy does not work for trading because it does not have a positive edge, which is a measure of the strategy's ability to make money. The manager also noted that some months were profitable, but this does not mean the strategy is good. The manager used data from https://runway.adityaperswal.workers.dev/data/stock-bars and was based on the economic hypothesis found in https://pages.stern.nyu.edu/~lpederse/papers/TimeSeriesMomentum.pdf.
A news scan found the notes of an automated trading fund manager. The manager tested a trading strategy on the DBC stock. The strategy had a 12-month adjusted return that was better than the BIL stock. The manager started with a regular open entry and did not reenter the trade within the month. The manager used a 20-session simple Average True Range, or ATR, which is a measure of how much a stock's price moves, to set the initial stop loss at 1 ATR, the target at 1.5 ATR, and the trailing distance at 1 ATR. The manager sampled the raw 15-minute closes at the end of each interval and used the sampled highs to adjust the stop loss. The manager exited the trade at the observed price after costs. The manager skipped targets that were more than 2 ADR, or Average Daily Range. The manager trained the strategy from 2017 to 2020 and tested it from 2021 to August 2026. The manager split the fee and slippage, which is the difference between the expected and actual price of a trade, equally at 10 basis points, or 0.1%, per side. The results showed a return of -1.1431%, a drawdown of 10.0758%, and a win rate of 37.931%. The manager concluded that this strategy did not have a positive edge and should not be used for trading. The manager cited a study by NYU and a data source from Aditya Perswal.
A news scan found the notes of a trading fund manager. The manager tested a trading strategy called QEX90704 on a fund called DBC. DBC is a type of investment fund, and its previous returns were higher than another fund called BIL. The manager started testing the strategy at the beginning of the month, without trying to catch up on any missed trades. The manager used a simple method to set the initial stop loss, target, and trailing distance, which is a way to automatically adjust the stop loss as the price moves. The manager used a 15-minute interval to sample the closing prices and adjust the stop loss. The manager also used a method called ATR, which stands for Average True Range, a measure of price volatility. The manager set the initial stop loss at 1 ATR, the target at 1.5 ATR, and the trailing distance at 1 ATR. The manager also set a timeout, which means the trade would be closed at the next monthly opening. The manager trained the strategy using data from 2017 to 2020 and tested it using data from 2021 to August 2026. The manager assumed a fee of 10 basis points, which is a measure of the cost of trading, per side. The results showed a return of -1.8176%, a drawdown of 7.0494%, and a win rate of 45.4545%. The manager concluded that this strategy does not have a positive edge, which means it is not likely to produce consistent profits. The manager also found that the strategy had a maximum drawdown of 29.7078% and a total return of 104.6964% over the testing period. The manager referenced a study on time series momentum, which is a way to predict price movements based on past trends.
A news scan found the headline about QEX90704 monthly_matched results. The fund manager analyzed the performance of DBC, a ticker symbol, and compared it to BIL, another ticker symbol. DBC's adjusted return for the previous 12 months was higher than BIL's. The manager used a strategy called a swing target, which is a price level that a stock must reach before the manager will sell it, with a gate of 2ADR20, meaning the stock price must not move more than twice the average daily range in the last 20 days. The manager tested this strategy using data from 2017 to 2020 and then from 2021 to August 2026. The results showed a return of 37.36428440482524 percent, a drawdown of 26.470485269856113 percent, and a win rate of 51.724137931034484 percent. The manager also reported that the strategy had an average win of 4.907200275789534 percent and an average loss of 2.7464593861411704 percent. The manager used a tool called Vectorbt, which is a backtesting framework, to simulate the strategy and found that it matched the expected results. The manager concluded that the strategy should be retained for research purposes only, and that monthly profitability does not necessarily mean that the strategy is working. The manager also noted that some months were profitable, but this does not validate the overall strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the economic hypothesis is based on a paper by NYU Stern.
A news scan found the notes from a trading fund manager. The fund's previous month return was higher than a benchmark called BIL. The fund started with a regular open entry and did not reenter during the month. The fund held its position for the full month without any restrictions. The fund's performance was tested using data from 2017 to 2020 and then from 2021 to August 2026. The test was done with a 10 basis point fee on each side, meaning 0.1% of the trade value was deducted as a fee. The results showed a return of 104.6964% and a maximum drawdown of 29.7078%. The fund was always long, meaning it only bought assets, and never sold short. The results were calculated using a method called vectorbt, which replays trades to test performance. The drawdown, or maximum loss, was calculated using 15-minute closing prices, including extended hours. The fund's performance was compared to a saved study, which showed a different return. The conclusion is to retain a research watch, meaning the fund's performance is being monitored but not used for actual trading decisions. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the economic hypothesis is based on a paper from NYU, https://pages.stern.nyu.edu/~lpederse/papers/TimeSeriesMomentum.pdf.