How to find the smallest transaction fees and commissions available and reduce trading overhead?
The lowest cost way to trade on an exchange is to trade directly on the exchange. I can't speak to the LSE, but in the US, there is a mandated firewall between the individual and the exchange, the broker; therefore, in the US, one would have to start a business and become a broker. If that process is too costly, the broker or trade platform that permits individuals to trade with the lowest commissions is the next lowest.
"I hope a wall of text with citations qualifies as ""relatively easy."" Many of these studies are worth quoting at length. Long story short, a great deal of research has found that actively-managed funds underperform market indexes and passively-managed funds because of their high turnover and higher fees, among other factors.  Longer answer: Chris is right in stating that survivorship bias presents a problem for such research; however, there are several academic papers that address the survivorship problem, as well as the wider subject of active vs. passive performance. I'll try to provide a brief summary of some of the relevant literature.  The seminal paper that started the debate is Michael Jensen's 1968 paper titled ""The Performance of Mutual Funds in the Period 1945-1964"". This is the paper where Jensen's alpha, the ubiquitous measure of the performance of mutual fund managers, was first defined. Using a dataset of 115 mutual fund managers, Jensen finds that The evidence on mutual fund performance indicates not only that these 115 mutual funds were on average not able to predict security prices well enough to outperform a buy-the-market-and-hold policy, but also that there is very little evidence that any individual fund was able to do significantly better than that which we expected from mere random chance. Although this paper doesn't address problems of survivorship, it's notable because, among other points, it found that managers who actively picked stocks performed worse even when fund expenses were ignored. Since actively-managed funds tend to have higher expenses than passive funds, the actual picture looks even worse for actively managed funds. A more recent paper on the subject, which draws similar conclusions, is Martin Gruber's 1996 paper ""Another puzzle: The growth in actively managed mutual funds"". Gruber calls it ""a puzzle"" that investors still invest in actively-managed funds, given that their performance on average has been inferior to that of index funds. He addresses survivorship bias by tracking funds across the entire sample, including through mergers. Since most mutual funds that disappear are merged into existing funds, he assumes that investors in a fund that disappear choose to continue investing their money in the fund that resulted from the merger. Using this assumption and standard measures of mutual fund performance, Gruber finds that mutual funds underperform an appropriately weighted average of the indices by about 65 basis points per year. Expense ratios for my sample averaged 113 basis points a year. These numbers suggest that active management adds value, but that mutual funds charge the investor more than the value added. Another nice paper is Mark Carhart's 1997 paper ""On persistence in mutual fund performance"" uses a sample free of survivorship bias because it includes ""all known equity funds over this period."" It's worth quoting parts of this paper in full: I demonstrate that expenses have at least a one-for-one negative impact on fund performance, and that turnover also negatively impacts performance. ... Trading reduces performance by approximately 0.95% of the trade's market value.  In reference to expense ratios and other fees, Carhart finds that The investment costs of expense ratios, transaction costs, and load fees all have a direct, negative impact on performance. The study also finds that funds with abnormally high returns last year usually have higher-than-expected returns next year, but not in the following years, because of momentum effects. Lest you think the news is all bad, Russ Wermer's 2000 study ""Mutual fund performance: An empirical decomposition into stock‐picking talent, style, transactions costs, and expenses"" provides an interesting result. He finds that many actively-managed mutual funds hold stocks that outperform the market, even though the net return of the funds themselves underperforms passive funds and the market itself.  On a net-return level, the funds underperform broad market indexes by one percent a year. Of the 2.3% difference between the returns on stock holdings and the net returns of the funds, 0.7% per year is due to the lower average returns of the nonstock holdings of the funds during the period (relative to stocks). The remaining 1.6% per year is split almost evenly between the expense ratios and the transaction costs of the funds. The final paper I'll cite is a 2008 paper by Fama and French (of the Fama-French model covered in business schools) titled, appropriately, ""Mutual Fund Performance"". The paper is pretty technical, and somewhat above my level at this time of night, but the authors state one of their conclusions bluntly quite early on: After costs (that is, in terms of net returns to investors) active investment is a negative sum game.  Emphasis mine. In short, expense ratios, transaction costs, and other fees quickly diminish the returns to active investment. They find that The [value-weight] portfolio of mutual funds that invest primarily in U.S. equities is close to the market portfolio, and estimated before fees and expenses, its alpha is close to zero. Since the [value-weight] portfolio of funds produces an α close to zero in gross returns, the alpha estimated on the net returns to investors is negative by about the amount of fees and expenses. This implies that the higher the fees, the farther alpha decreases below zero. Since actively-managed mutual funds tend to have higher expense ratios than passively-managed index funds, it's safe to say that their net return to the investor is worse than a market index itself. I don't know of any free datasets that would allow you to research this, but one highly-regarded commercial dataset is the CRSP Survivor-Bias-Free US Mutual Fund Database from the Center for Research in Security Prices at the University of Chicago. In financial research, CRSP is one of the ""gold standards"" for historical market data, so if you can access that data (perhaps for a firm or academic institution, if you're affiliated with one that has access), it's one way you could run some numbers yourself." You'd need millions of dollars to trade the number of shares it would take to profit from these penny variations.  What you bring up here is the way high frequency firms front-run trades and profit on these pennies. Say you have a trade commission of $5.  Every time you buy you pay $5, every time you sell you pay $5.  So you need a gain in excess of $10, a 10% gain on $100.  Now if you wanted to trade on a penny movement from $100 to $100.01, you need to have bought 1,000 shares totaling $100,000 for the $0.01 price movement to cover your commission costs.  If you had $1,000,000 to put at risk, that $0.01 price movement would net you $90 after commission, $10,000,000 would have made you $990.  You need much larger gains at the retail level because commissions will equate to a significant percentage of the money you're investing. Very large trading entities have much different arrangements and costs with the exchanges.  They might not pay a fee on each transaction but something that more closely resembles a subscription fee, and costs something that more closely resembles a house. Now to your point, catching these price movements and profiting.  The way high frequency trading firms purportedly make money relates to having a very low latency network connection to a particular exchange.  Their very low latency/very fast network connection lets them see orders and transact orders before other parties.   Say some stock has an ask at $101 x 1,000 shares.  The next depth is $101.10.  You see a market buy order come in for 1,000 shares and place a buy order for 1,000 shares at $101 which hits the exchange first, then immediately place a sell order at $101.09, changing the ask from $101.00 to $101.09 and selling in to the market order for a gain of $0.09 per share. "A couple options that I know of: Interactive Brokers offers a ""paper trading"" mode to its account holders that allows you to start with a pretend stack of money and place simulated trades to test trading ideas. They also provide an API that allows you to interface with their platform programmatically for retrieving quotes, placing orders, and the such. As you noted, however, it's not free; you must hold a funded brokerage account in order to qualify for access to their platform. In order to maintain an account, there are minimums for required equity and monthly activity (measured in dollars that you spend on commissions), so you won't get access to their platform without having a decent amount of skin in the game. IB's native API is Java-based; IbPy is an unofficial wrapper that makes the interface available in Python. I've not used IB at all myself, but I've heard good things about their API and its accessibility via IbPy. Edit: IB now supports Python natively via their published API, so using IbPy is no longer needed, unless you wish to use Python 2.x. The officially supported API is based on Python 3. TD Ameritrade also offers an API that is usable by its brokerage clients. They do not offer any such ""paper trading"" mode, so you would need to ""execute"" transactions based on quotes at the corresponding trade times and then keep track of your simulated account history yourself. The API supports quote retrieval, price history, and trade execution, among other functions. TDA might be more attractive than IB if you're looking for a low-cost link into market data, as I believe their minimum-equity levels are lower. To get access, you'll need to sign up for an API developer account, which I believe requires an NDA. I don't believe there is an official Python implementation of the API, but if you're a capable Python writer, you shouldn't have trouble hooking up to the published interfaces. Some caveats: as when doing any strategy backtesting, you'll want to be sure to be pessimistic when doing so, so your optimism doesn't make your trades look more successful than they would be in the real world. At a minimum, you'll want to ensure that your simulations transact at the posted bid/ask prices, not necessarily the last trade's price, as well as any commissions and fees associated with the trade. A more robust scheme would also take into account the depth of the order book (also known as level 2 quotes), which can cause additional slippage in the prices at which you buy/sell your security. An even more robust scheme would take into account the potential latency of trade execution, looking at all prices over some time period that covers the maximum expected latency and simulating the trade at the worst-possible price." "There are basically two different markets for ADRs and ordinary shares. 1) The American market, 2) the ""local"" market. The following is not true for most stocks in ""developed"" markets. But it is often true that the American market (for ADRs) is far more liquid than the local market for ordinary shares of a developing country. For instance, there was a time when the ADRs of Telmex (Telefonos of Mexico) was the fifth most traded stock in the world, after Exxon (before its merger with Mobil), IBM, Microsoft, and A T&T, meaning that it was easy to trade with low fees on the NYSE. It was much harder and slower to buy the local shares of Telmex in Mexico, on the Mexican exchange. Also, the accompanying currency transactions were harder to execute with the ord, because you have to settle in local currency and pay an FX commission. With the ADR, the exchange rate is ""built"" into the (dollar) price, and you settle in dollars." In my experience thanks to algorithmic trading the variation of the spread and the range of trading straight after a major data release will be as random as possible, since we live in an age that if some pattern existed at these times HFT firms would take out any opportunity within nanoseconds. Remember that some firms write algorithms to predict other algorithms, and it is at times like those that this strategy would be most effective. With regards to my own trading experience I have seen orders fill almost €400 per contract outside of the quoted range, but this is only in the most volatile market conditions. Generally speaking, event investing around numbers like these are only for top wall street firms that can use co-location servers and get a ping time to the exchange of less than 5ms. Also, after a data release the market can surge/plummet in either direction, only to recover almost instantly and take out any stops that were in its path. So generally, I would say that slippage is extremely unpredictable in these cases( because it is an advantage to HFT firms to make it so ) and stop-loss orders will only provide limited protection. There is stop-limit orders( which allow you to specify a price limit that is acceptable ) on some markets and as far as I know InteractiveBrokers provide a guaranteed stop-loss fill( For a price of course ) that could be worth looking at, personally I dont use IB.  I hope this answer provides some helpful information, and generally speaking, super-short term investing is for algorithms. Small purchases will have a disproportionate expense for commissions. Even a $5 trade fee is 5% on a $100 purchase. So on one hand, it's common to advise individuals just starting out to use mutual funds, specifically index funds with low fees. On the flip side, holding stocks has no annual fee, and if you are buying for the long term, you may still be better off with an eye toward cost, and learn over time.  In theory, an individual stands a better chance to beat the experts for a number of reasons, no shareholders to answer to, and the ability to purchase without any disclosure, among them. In reality, most investor lag the average by such a wide margin, they'd be best off indexing and staying in for the long term. "Check the transactions costs, ""Desk fees"" and the whole structure, sit down with them and list everything.  Then make a spreadsheet and calculate all the stuff they hit you with and figure out how good you have to be in trading to make money, (in terms of accuracy and p/l)." Why not figure out the % composition   of the index and invest in the   participating securities directly? This isn't really practical.   Two indices I use follow the Russell 2000 and the S&P 500 Those two indices represent 2500 stocks. A $4 brokerage commission per trade would mean that it would cost me $10,000 in transaction fees to buy a position in 2500 stocks. Not to mention, I don't want to track 2500 investments. Index funds provide inexpensive diversity.